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* version update for next release
* Add Kitti object detection dataset (#128)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* address reviews
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* Add Waymo Dataset (#136)
* add waymo preprocess
* add waymo class
* added argparse
* apply style
* add docstring
* remove cv2
* Add NuScenes dataset (#137)
* add preprocess nuscenes
* bug fix
* add argparse
* add nuscenes class
* apply style
* added label
* Fix ignore class
* add docstring
* style
* create sampler class for sampling point cloud idx and points idx (#135)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* sampler
* sampler class
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* spatially regular
* kpconv sampler
* kpconv paris lille
* delete debug information
* valid
* vlida
* default sampler
* confusion matrix
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
* Add Lyft Dataset (#138)
* bug fix
* Added preprocess lyft
* bug
* add dataset_class for lyft
* style
* bug fix
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
* apply-style
* Download scripts (#145)
* add kitti download script
* fix semantickitti
* added lyft download script
* Add bounding boxes to visualizer (#140)
* Add bounding boxes to visualizer
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* Create LICENSE
* Change Bounding box class. (#149)
* change bbox class
* improve bbox
* style
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* fix waymo bbox
* fix kitti bbox
* apply style;
* address review
* Update README.md
* Added Agroverse 3D Dataset (#155)
* added argoverse
* style
* change classes
* use open3d for reading pcd
* link to kpconv parislille3d models in readme (#160)
* readme for parislille3d kpconv models
* PointPillars inference pipeline (#153)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* using our checkpoint format
* fix ci
* apply style
* apply style
* changed list to tuple
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* add comments for visualize predictions (#151)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* Import os (#148)
I am getting also another error later about
NameError Traceback (most recent call last)
<ipython-input-5-bed5d0e06c9a> in <module>
22 pipeline_k.load_ckpt(model.cfg.ckpt_path)
23
---> 24 data_path = os.path.dirname(os.path.realpath(__file__)) + "/demo_data"
25 pc_names = ["000700", "000750"]
26
NameError: name '__file__' is not defined
for this
data_path = os.path.dirname(os.path.realpath(__file__)) + "/demo_data"
* Create LICENSE
* add comments
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: Albhox <albertotono3@gmail.com>
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
* Fix broken link to torch RandLA-Net Toronto 3d model (#163)
* Updating documentation (#154)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding jupyter tutorials for: Training SS using PyTorch and Traing SS using TF
* Update customdataset.py
Fixing indentation issue.
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
* Add Tensorflow model and inference pipeline. (#159)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* apply style
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: Lukas Prantl <lukas_prantl@hotmail.de>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* Fixes for changes from TensorList to Tesor for t.geometry objects (#161)
* Yiling/readme randlanet semantic3d (#167)
* readme for randlanet semantic3d models
* readme change
* Prantl/point pillars train (#170)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* using our checkpoint format
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* apply style
* implemented loss calculation of pointpillars, not yet tested
* training working, evaluation and augmentation missing
* fixed batched inference
* style
* new bev bounding box class
* validation added
* small fixes
* small fix
* reset yaw transformation in kitti bbox
* fixed bug in loss calculation, fixed bug in save logs
* removed debugging bug
* fixed cls loss bug
* style
* Update kitti.py
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* Prantl/point pillars train tf (#171)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* using our checkpoint format
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* apply style
* implemented loss calculation of pointpillars, not yet tested
* training working, evaluation and augmentation missing
* fixed batched inference
* style
* new bev bounding box class
* validation added
* small fixes
* small fix
* reset yaw transformation in kitti bbox
* fixed bug in loss calculation, fixed bug in save logs
* removed debugging bug
* fixed cls loss bug
* trainings pipeline for tensorflow
* style
* style
* Update kitti.py
* style
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* new validation for torch (#169)
* infer
* test and infer
* testing
* test inference
* modify
* before merge
* inference dummy
* add back save results
* update model zoo (#175)
* added weight initialization (#174)
* Added ShapeNet dataset (#157)
* Added ShapeNet dataset
* Applied style
* Added dummy part segmentation labels
This is a hack as there aren't any official part labels (as far as I know).
Co-authored-by: Matthias Humt <matthias.humt@dlr.de>
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
* Fix for changes in t::geometry (#173)
* Add wide lines (#176)
* Prantl/point pillars metrics (#172)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* setup
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added temporary metric test + numba operators
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* add mAP metric
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* using our checkpoint format
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* fix ci
* apply style
* apply style
* apply style
* implemented loss calculation of pointpillars, not yet tested
* changed list to tuple
* training working, evaluation and augmentation missing
* fixed batched inference
* style
* using same mAP technique as mmdet, 1 percent off
* new bev bounding box class
* validation added
* small fixes
* small fix
* reset yaw transformation in kitti bbox
* fixed bug in loss calculation, fixed bug in save logs
* removed debugging bug
* undone removing debug code for test run
* old loader
* some small fixes
* cls loss fixed
* undone changes in demo script
* fixed cls loss bug
* small bug fix in mAP calculation
* metric bug fixes, o3d iou intergrated
* trainings pipeline for tensorflow
* fixed cumulative prediction calculation of mAP, no more deviation in mAP calculation, removed debugging code
* mAP validation added to tf model
* fixed convertion to eval data
* small fixes
* fixed infinite epoch
* style
* style
* iou gpu/cpu depending on o3d build
* Update kitti.py
* replaced adamW with adam in tf training pipeline
* removed legacy setup
* style
* fixed bug in loss computation of tf model, fixed bug in scatter operation of pointpillars tf
* renamed tensorboard writer
* tf summary writer
* tf summary fix
* mAP in tensorboard
* fixed tf writer
* fixed some merge artifacts
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* Filter kitti point cloud (#177)
* reduce kitti pc
* apply style
* PointPillars bug fixes (#179)
* fixed resuming from checkpoint
* fixed offset in reassume
* fixed missing device definition in pointpillars
* Update object_detection.py
removed debug log
* Data Augmentation (#178)
* shuffle
* object range filter
* add sample objects
* added collect bbox
* add box points in preprocessing
* add object sample
* add augment in config
* bug fixes
* apply style
* remove duplicate
* filter by min points
* apply style
* improve speed
* fix tf
* apply style
* optional out_path
* vectorization of points in shape, small bug fixes, removed pickle path
Co-authored-by: praluk <lukas_prantl@hotmail.de>
* fixed absolute path bug (#182)
* Disable data augmentation while testing. (#181)
* disable test augment
* - validation without augmentation
- transform returns bbox_obj
- labels and bboxes single elements instead of list
- fixed ignored min_points
Co-authored-by: Lukas Prantl <lukas_prantl@hotmail.de>
* update readme and config files for parislille3d; align points for parislille3d (#180)
* randlanet parislille
* config
* merge
* model path
* minor changes
* trans normalize
* trans norm
* fixed infinte dataset iteration (#184)
* fixed infinte dataset iteration
* - fixed obj det demo
- preprocess full points
* style
* fix collision (#183)
* Abhishek/documentation (#185)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding jupyter tutorials for: Training SS using PyTorch and Traing SS using TF
* Update customdataset.py
Fixing indentation issue.
* Updates to readme
Added object detection updates to the readme.md in Open3D ML and Open3D ML\Script for the new release.
* Update __init__.py
* Fixing style errors
Fixing style errors.
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
* In dataset mode, only bounding boxes from visible names are visible. Also update set_background_color() -> set_background() (#186)
* Fix absolute path. (#187)
* fix abs path
* fix order of paths
* fix skewed argoverse
* Abhishek/documentation (#188)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding jupyter tutorials for: Training SS using PyTorch and Traing SS using TF
* Update customdataset.py
Fixing indentation issue.
* Updates to readme
Added object detection updates to the readme.md in Open3D ML and Open3D ML\Script for the new release.
* Update __init__.py
* Fixing style errors
Fixing style errors.
* Updating Readme
Updating readme to include image for visualization.
* Replacing bounding_boxes image.
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
* Prantl/dataset fixes (#189)
* - Object3D of datasets inherit from BEVBox, calib unified, output path of preprocessing scripts optional, label names instead of numbers
* dataset configs
* small bugfixes
* fixes for Lyft training
* added missing file
* small bugfixes
* added sample split
* style
* Update .gitignore
* Fix Label LUT and Waymo (#190)
* fix lut
* fix waymo
* address review
* fixed style for mAP log in tf (#191)
* Change the line width factor now that line widths are working (#192)
* Prantl/pointpillars readme (#193)
* updated pointpillar metrics
* updated weights
* object sampler fix (#194)
* upload link (#195)
* Added CI tests for Object detection (#208)
* Dev (#197)
* version update for next release
* Add Kitti object detection dataset (#128)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* address reviews
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* Add Waymo Dataset (#136)
* add waymo preprocess
* add waymo class
* added argparse
* apply style
* add docstring
* remove cv2
* Add NuScenes dataset (#137)
* add preprocess nuscenes
* bug fix
* add argparse
* add nuscenes class
* apply style
* added label
* Fix ignore class
* add docstring
* style
* create sampler class for sampling point cloud idx and points idx (#135)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* sampler
* sampler class
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* spatially regular
* kpconv sampler
* kpconv paris lille
* delete debug information
* valid
* vlida
* default sampler
* confusion matrix
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
* Add Lyft Dataset (#138)
* bug fix
* Added preprocess lyft
* bug
* add dataset_class for lyft
* style
* bug fix
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
* apply-style
* Download scripts (#145)
* add kitti download script
* fix semantickitti
* added lyft download script
* Add bounding boxes to visualizer (#140)
* Add bounding boxes to visualizer
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* Create LICENSE
* Change Bounding box class. (#149)
* change bbox class
* improve bbox
* style
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* fix waymo bbox
* fix kitti bbox
* apply style;
* address review
* Update README.md
* Added Agroverse 3D Dataset (#155)
* added argoverse
* style
* change classes
* use open3d for reading pcd
* link to kpconv parislille3d models in readme (#160)
* readme for parislille3d kpconv models
* PointPillars inference pipeline (#153)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* using our checkpoint format
* fix ci
* apply style
* apply style
* changed list to tuple
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* add comments for visualize predictions (#151)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* Import os (#148)
I am getting also another error later about
NameError Traceback (most recent call last)
<ipython-input-5-bed5d0e06c9a> in <module>
22 pipeline_k.load_ckpt(model.cfg.ckpt_path)
23
---> 24 data_path = os.path.dirname(os.path.realpath(__file__)) + "/demo_data"
25 pc_names = ["000700", "000750"]
26
NameError: name '__file__' is not defined
for this
data_path = os.path.dirname(os.path.realpath(__file__)) + "/demo_data"
* Create LICENSE
* add comments
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: Albhox <albertotono3@gmail.com>
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
* Fix broken link to torch RandLA-Net Toronto 3d model (#163)
* Updating documentation (#154)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding jupyter tutorials for: Training SS using PyTorch and Traing SS using TF
* Update customdataset.py
Fixing indentation issue.
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
* Add Tensorflow model and inference pipeline. (#159)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* apply style
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: Lukas Prantl <lukas_prantl@hotmail.de>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* Fixes for changes from TensorList to Tesor for t.geometry objects (#161)
* Yiling/readme randlanet semantic3d (#167)
* readme for randlanet semantic3d models
* readme change
* Prantl/point pillars train (#170)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* using our checkpoint format
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* apply style
* implemented loss calculation of pointpillars, not yet tested
* training working, evaluation and augmentation missing
* fixed batched inference
* style
* new bev bounding box class
* validation added
* small fixes
* small fix
* reset yaw transformation in kitti bbox
* fixed bug in loss calculation, fixed bug in save logs
* removed debugging bug
* fixed cls loss bug
* style
* Update kitti.py
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* Prantl/point pillars train tf (#171)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* using our checkpoint format
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* apply style
* implemented loss calculation of pointpillars, not yet tested
* training working, evaluation and augmentation missing
* fixed batched inference
* style
* new bev bounding box class
* validation added
* small fixes
* small fix
* reset yaw transformation in kitti bbox
* fixed bug in loss calculation, fixed bug in save logs
* removed debugging bug
* fixed cls loss bug
* trainings pipeline for tensorflow
* style
* style
* Update kitti.py
* style
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* new validation for torch (#169)
* infer
* test and infer
* testing
* test inference
* modify
* before merge
* inference dummy
* add back save results
* update model zoo (#175)
* added weight initialization (#174)
* Added ShapeNet dataset (#157)
* Added ShapeNet dataset
* Applied style
* Added dummy part segmentation labels
This is a hack as there aren't any official part labels (as far as I know).
Co-authored-by: Matthias Humt <matthias.humt@dlr.de>
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
* Fix for changes in t::geometry (#173)
* Add wide lines (#176)
* Prantl/point pillars metrics (#172)
* Fix missing argument
* more import bugfixes (#126)
* fix missing attribute 'utils'
* removed import statements for unused modules
* fix conflict
* add kitti dataset
* add readme about config files (#129)
* readme about config files
* link to predefined script
* Update README.md
* address reviews
* Simplified install instructions (#127)
* Simpliefied install instructions
Multiple requirements files for different use cases.
* minor change to README
* minor change adding link to docs for build from source.
* update install instruction, add +cpu to torch requirements.txt
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
* added conditionals in requirements-torch for macos (#131)
* Fix color dictionary in Semantic-KITTI bgr -> rgb (#130)
* Enable hierarchy UI feature in visualizer for SemanticKITTI (#133)
* PointPillars port from mmdet without NMS and Voxelizer. Only inference. Focal loss and smooth L1 implemented and tested.
* loss function implemented and tested
* Add bounding boxes to visualizer
* Voxelization layer for point_pillars
* apply style
* Dataset bounding boxes are now fully their own object and do hide auto-hide/show pieces associated with a geometry, but they do auto-hide irrelevant boxes when animating.
* Style fixes
* objdet metric
* removed mmdet3d dependency, o3d nms and voxelizer added
* renamed objdet to run_pipeline, implemented part of the objdet pipeline
* Added documentation, changed definition of size argument to be edge-to-edge and adjusted code accordingly
* kitti eval only 3d boxes
* test pipeline point pillars
* howto small fix
* change bbox class
* improve bbox
* style
* visualization of results
* test vis of results
* using new bounding box class
* waymo bbox
* added nuscenes bbox
* added lyft bbox
* apply style
* anchor head refactoring
* fix waymo bbox
* fix kitti bbox
* apply style;
* removed transformation of the predicted boxes
* added licenses to files
* removed unnecessary parts
* format
* setup
* Labels displayed in visualization. Kitti dataset label ordering changed to match MMDetection3D.
* added temporary metric test + numba operators
* added tf voxel class
* added PFNlayer
* add pillar feature net
* added pointpillar scatter class
* add class SECOND
* added SECONDFPN layer
* fix ragged tensor
* add mAP metric
* implemented tf bbox generator, improved some torch helpers of pointpillars
* batch support for box generation
* add call method
* fix name conflicts
* fix bugs
* added tf objdet pipeline
* fix scatter tf
* fix conv2d channels last
* apply style
* added crossentropy
* add focal loss
* added smooth L1 loss
* apply style
* using our checkpoint format
* fix batchnorm
* add load/save ckpt
* apply style
* simplify inference torch
* simplify inference tf
* fix bug
* fix ci
* fix ci tf
* fix ci
* apply style
* apply style
* apply style
* implemented loss calculation of pointpillars, not yet tested
* changed list to tuple
* training working, evaluation and augmentation missing
* fixed batched inference
* style
* using same mAP technique as mmdet, 1 percent off
* new bev bounding box class
* validation added
* small fixes
* small fix
* reset yaw transformation in kitti bbox
* fixed bug in loss calculation, fixed bug in save logs
* removed debugging bug
* undone removing debug code for test run
* old loader
* some small fixes
* cls loss fixed
* undone changes in demo script
* fixed cls loss bug
* small bug fix in mAP calculation
* metric bug fixes, o3d iou intergrated
* trainings pipeline for tensorflow
* fixed cumulative prediction calculation of mAP, no more deviation in mAP calculation, removed debugging code
* mAP validation added to tf model
* fixed convertion to eval data
* small fixes
* fixed infinite epoch
* style
* style
* iou gpu/cpu depending on o3d build
* Update kitti.py
* replaced adamW with adam in tf training pipeline
* removed legacy setup
* style
* fixed bug in loss computation of tf model, fixed bug in scatter operation of pointpillars tf
* renamed tensorboard writer
* tf summary writer
* tf summary fix
* mAP in tensorboard
* fixed tf writer
* fixed some merge artifacts
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
* Filter kitti point cloud (#177)
* reduce kitti pc
* apply style
* PointPillars bug fixes (#179)
* fixed resuming from checkpoint
* fixed offset in reassume
* fixed missing device definition in pointpillars
* Update object_detection.py
removed debug log
* Data Augmentation (#178)
* shuffle
* object range filter
* add sample objects
* added collect bbox
* add box points in preprocessing
* add object sample
* add augment in config
* bug fixes
* apply style
* remove duplicate
* filter by min points
* apply style
* improve speed
* fix tf
* apply style
* optional out_path
* vectorization of points in shape, small bug fixes, removed pickle path
Co-authored-by: praluk <lukas_prantl@hotmail.de>
* fixed absolute path bug (#182)
* Disable data augmentation while testing. (#181)
* disable test augment
* - validation without augmentation
- transform returns bbox_obj
- labels and bboxes single elements instead of list
- fixed ignored min_points
Co-authored-by: Lukas Prantl <lukas_prantl@hotmail.de>
* update readme and config files for parislille3d; align points for parislille3d (#180)
* randlanet parislille
* config
* merge
* model path
* minor changes
* trans normalize
* trans norm
* fixed infinte dataset iteration (#184)
* fixed infinte dataset iteration
* - fixed obj det demo
- preprocess full points
* style
* fix collision (#183)
* Abhishek/documentation (#185)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding jupyter tutorials for: Training SS using PyTorch and Traing SS using TF
* Update customdataset.py
Fixing indentation issue.
* Updates to readme
Added object detection updates to the readme.md in Open3D ML and Open3D ML\Script for the new release.
* Update __init__.py
* Fixing style errors
Fixing style errors.
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
* In dataset mode, only bounding boxes from visible names are visible. Also update set_background_color() -> set_background() (#186)
* Fix absolute path. (#187)
* fix abs path
* fix order of paths
* fix skewed argoverse
* Abhishek/documentation (#188)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding jupyter tutorials for: Training SS using PyTorch and Traing SS using TF
* Update customdataset.py
Fixing indentation issue.
* Updates to readme
Added object detection updates to the readme.md in Open3D ML and Open3D ML\Script for the new release.
* Update __init__.py
* Fixing style errors
Fixing style errors.
* Updating Readme
Updating readme to include image for visualization.
* Replacing bounding_boxes image.
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
* Prantl/dataset fixes (#189)
* - Object3D of datasets inherit from BEVBox, calib unified, output path of preprocessing scripts optional, label names instead of numbers
* dataset configs
* small bugfixes
* fixes for Lyft training
* added missing file
* small bugfixes
* added sample split
* style
* Update .gitignore
* Fix Label LUT and Waymo (#190)
* fix lut
* fix waymo
* address review
* fixed style for mAP log in tf (#191)
* Change the line width factor now that line widths are working (#192)
* Prantl/pointpillars readme (#193)
* updated pointpillar metrics
* updated weights
* object sampler fix (#194)
* upload link (#195)
Co-authored-by: Sanskar Agrawal <sanskaragrawal107@gmail.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: Prantl <lukas_prantl@hotmail.de>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
Co-authored-by: KENTO Yamamoto <31678561+kento-Y@users.noreply.github.com>
Co-authored-by: Albhox <albertotono3@gmail.com>
Co-authored-by: amirshal <35299570+amirshal@users.noreply.github.com>
Co-authored-by: AbhishekS <65679171+ClarytyLLC@users.noreply.github.com>
Co-authored-by: Matthias Humt <22399283+hummat@users.noreply.github.com>
Co-authored-by: Matthias Humt <matthias.humt@dlr.de>
* do not import CUDA functions when CUDA device is not available (#198)
* Do not import CUDA functions when CUDA device is not available
* update name
* update wording
* Use scikit-learn instead of sklearn (#200)
* torch test
* tf objdet test
* apply style
Co-authored-by: germanros1987 <38517452+germanros1987@users.noreply.github.com>
Co-authored-by: Benjamin Ummenhofer <benjaminum@gmail.com>
Co-authored-by: YilingQiao <49262224+YilingQiao@users.noreply.github.com>
Co-authored-by: ssheorey <41028320+ssheorey@users.noreply.github.com>
Co-authored-by: Ignacio Vizzo <ignaciovizzo@gmail.com>
Co-authored-by: prewettg <prewettg@gmail.com>
Co-authored-by: Prantl <lukas_prantl@hotmail.de>
Co-authored-by: lprantl <lprantl@lprantl-DESK.imu.intel.com>
Co-authored-by: KENTO Yamamoto <31678561+kento-Y@users.noreply.github.com>
Co-authored-by: Albhox <albertotono3@gmail.com>
Co-authored-by: amirshal <35299570+amirshal@users.noreply.github.com>
Co-authored-by: AbhishekS <65679171+ClarytyLLC@users.noreply.github.com>
Co-authored-by: Matthias Humt <22399283+hummat@users.noreply.github.com>
Co-authored-by: Matthias Humt <matthias.humt@dlr.de>
Co-authored-by: Yixing Lao <yixing.lao@gmail.com>
* Added Scannet dataset (#212)
* add scannet resources
* added preprocess scannet
* change init
* add class mapping resource
* add scannet class
* apply style
* Add Bounding boxes in S3DIS (#210)
* modify s3dis
* find min bbox
* add bev box
* apply style
* Add SunRGBD Dataset (#215)
* sunrgbd download script
* added preprocess sunrgbd
* add data class for sunrgbd
* modify download script
* apply style
* remove folder
* Abhishek/documentation (#206)
* Editing documentation for TF and Torch dataloaders.
* Updating datasets documentation
* Updates after fixing style issues
* Updating semantic segmentation
* Updating dataset.py to fix semantic issues
* Updating semantic segmentation
* Updating base_dataset.py to fix semantic issues
* Updating custom_dataset.py to fix semantic issues
* Updating dataset.py to fix semantic issues
* Updating visualizer documentation
Updating files for visualizer documentation
* Update customdataset.py
* Fixing PR comment
Fixing indentation issue
* fix indent
* Updating customdataset comments
Updating customdataset to fix indentation issue.
* Adding Jupyter tutorials
Adding ju…- Loading branch information
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May 28, 2021
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