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).

[Paper] [Supp] [ArXiv]

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

Overview

• Model Zoo
• Installation
• Getting Started

Model Zoo

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.

Installation

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.

Getting Started

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

Important: Generate approximated complete object points

Under pcdet directory, execute:

python -m pcdet.datasets.multifindbestfit

License

PG-RCNN is released under the Apache 2.0 license.

Acknowledgement

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