feat(lidar_centerpoint): remove files those are no longer supported nor used (autowarefoundation#6497)

* feat(lidar_centerpoint): remove files those are no longer supported nor used

Signed-off-by: kminoda <koji.minoda@tier4.jp>

* remove unnecessary file

Signed-off-by: kminoda <koji.minoda@tier4.jp>

---------

Signed-off-by: kminoda <koji.minoda@tier4.jp>
Co-authored-by: Kenzo Lobos Tsunekawa <kenzo.lobos@tier4.jp>
Signed-off-by: kaigohirao <kaigo.hirao@proxima-ai-tech.com>

Author: 2 people

perception/lidar_centerpoint/CMakeLists.txt

@@ -135,25 +135,6 @@ if(TRT_AVAIL AND CUDA_AVAIL AND CUDNN_AVAIL)
     EXECUTABLE lidar_centerpoint_node
   )
 
-  ## single inference node ##
-  ament_auto_add_library(single_inference_lidar_centerpoint_component SHARED
-    src/single_inference_node.cpp
-  )
-
-  target_link_libraries(single_inference_lidar_centerpoint_component
-    centerpoint_lib
-  )
-
-  rclcpp_components_register_node(single_inference_lidar_centerpoint_component
-    PLUGIN "centerpoint::SingleInferenceLidarCenterPointNode"
-    EXECUTABLE single_inference_lidar_centerpoint_node
-  )
-
-  install(PROGRAMS
-    scripts/lidar_centerpoint_visualizer.py
-    DESTINATION lib/${PROJECT_NAME}
-  )
-
   ament_export_dependencies(ament_cmake_python)
 
   ament_auto_package(

perception/lidar_centerpoint/README.md

@@ -68,18 +68,6 @@ You can download the onnx format of trained models by clicking on the links belo
 `Centerpoint` was trained in `nuScenes` (~28k lidar frames) [8] and TIER IV's internal database (~11k lidar frames) for 60 epochs.
 `Centerpoint tiny` was trained in `Argoverse 2` (~110k lidar frames) [9] and TIER IV's internal database (~11k lidar frames) for 20 epochs.
 
-## Standalone inference and visualization
-
-In addition to its use as a standard ROS node, `lidar_centerpoint` can also be used to perform inferences in an isolated manner.
-To do so, execute the following launcher, where `pcd_path` is the path of the pointcloud to be used for inference.
-
-```bash
-ros2 launch lidar_centerpoint single_inference_lidar_centerpoint.launch.xml pcd_path:=test_pointcloud.pcd detections_path:=test_detections.ply
-```
-
-`lidar_centerpoint` generates a `ply` file in the provided `detections_path`, which contains the detections as triangle meshes.
-These detections can be visualized by most 3D tools, but we also integrate a visualization UI using `Open3D` which is launched alongside `lidar_centerpoint`.
-
 ### Changelog
 
 #### v1 (2022/07/06)