PG-RCNN: Semantic Surface Point Generation for 3D Object Detection (ICCV 2023)

This is the official implementation of "PG-RCNN: Semantic Surface Point Generation for 3D Object Detection" (ICCV 2023).

Thanks to OpenPCDet, our implementation is based of pcdet v0.5.2.

Overview

The results are the 3D detection performance of moderate difficulty on the val set of KITTI dataset.

	training time	Car@R40	Pedestrian@R40	Cyclist@R40	download
PGRCNN	~4.5 hours	85.25	58.37	75.04	model-8.8M

Note that the performance may vary a little due to sampling in PointNet++ encoder.

Please refer to INSTALL.md for the installation of OpenPCDet.

To train PG-RCNN, You need to additionally install pytorch3d for utilizing Chamfer Distance. We recommend using pytorch3d ver0.7.0.

Please refer to GETTING_STARTED.md to learn more usage about this project.

Under pcdet directory, execute:

python -m pcdet.datasets.multifindbestfit

PG-RCNN is released under the Apache 2.0 license.

We would like to thank the authors of OpenPCDet and BtcDet for their open source release of their codebase.