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Copy file name to clipboardexpand all lines: perception/autoware_lidar_centerpoint/README.md
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# lidar_centerpoint
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# autoware_lidar_centerpoint
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## Purpose
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lidar_centerpoint is a package for detecting dynamic 3D objects.
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autoware_lidar_centerpoint is a package for detecting dynamic 3D objects.
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## Inner-workings / Algorithms
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### The `build_only` option
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The `lidar_centerpoint` node has `build_only` option to build the TensorRT engine file from the ONNX file.
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The `autoware_lidar_centerpoint` node has `build_only` option to build the TensorRT engine file from the ONNX file.
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Although it is preferred to move all the ROS parameters in `.param.yaml` file in Autoware Universe, the `build_only` option is not moved to the `.param.yaml` file for now, because it may be used as a flag to execute the build as a pre-task. You can execute with the following command:
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#### Convert CenterPoint PyTorch model to ONNX Format
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The lidar_centerpoint implementation requires two ONNX models as input the voxel encoder and the backbone-neck-head of the CenterPoint model, other aspects of the network,
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The autoware_lidar_centerpoint implementation requires two ONNX models as input the voxel encoder and the backbone-neck-head of the CenterPoint model, other aspects of the network,
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such as preprocessing operations, are implemented externally. Under the fork of the mmdetection3d repository,
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we have included a script that converts the CenterPoint model to Autoware compatible ONNX format.
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You can find it in `mmdetection3d/projects/AutowareCenterPoint` file.
Create a new config file named **centerpoint_custom.param.yaml** under the config file directory of the lidar_centerpoint node. Sets the parameters of the config file like
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Create a new config file named **centerpoint_custom.param.yaml** under the config file directory of the autoware_lidar_centerpoint node. Sets the parameters of the config file like
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point_cloud_range, point_feature_size, voxel_size, etc. according to the training config file.
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```yaml
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