PanoOcc

PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation [paper]

News

[2024/2/27] PanoOcc is accepted by CVPR 2024
[2023/6/29] Code initialization, support occupancy prediction
[2023/6/16] We release the arXiv version (Paper in arXiv)

Catalog

3D Panoptic Segmentation (Waymo)
Sparse Decoder, Panoptic Refine
3D Panoptic Segmentation (nuScenes)
Occupancy Prediction (Occ3D-nuScenes)
Initialize

Introduction

Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentation, depth & elevation estimation, and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development procedure at the cost of losing an end-to-end unified solution to the problem. In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this, we introduce a novel method called PanoOcc, which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme, integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to verify the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore, our method can be easily extended to dense occupancy prediction and has shown promising performance on the Occ3D benchmark.

Getting Started

Performance

3D occupancy prediction

Occ3D-nuScenes: Occupancy Prediction (CVPR2023 WorkShop)
nus-det pretrain is the fcos3d pretrain on nuScenes detection, widely used in FCOS3D, BEVFormer
Download the nus-seg pretrain here: https://pan.baidu.com/s/1nx3wpQ96fF7R0OVNHSr1Zg fwcp

Backbone	Config	Image Size	Epochs	Pretrain	Memory	mIoU	checkpoints
R101-DCN	Pano-small	0.5x	12	nus-det	14 G	36.63	model
R101-DCN	Pano-base	1.0x	24	nus-det	35 G	41.60	model
R101-DCN	Pano-base-pretrain	1.0x	24	nus-seg	35 G	42.13	model

3D panoptic segmentation

nuScenes: LiDAR Semantic Segmentation (Validation)

Backbone	Config	Image Size	Epochs	Pretrain	Memory	mIoU	mAP	NDS	checkpoints
R50	Pano-small-1f	0.5x	24	ImageNet	16G	0.667	0.295	0.348	model
R50	Pano-small-4f	0.5x	24	ImageNet	18G	0.682	0.331	0.421	model
R101	Pano-base-4f	1.0x	24	nus-det	24G	0.712	0.411	0.497	model

nuScenes: LiDAR Semantic Segmentation (Test)

Backbone	Config	Image Size	Epochs	Pretrain	mIoU
R101	Pano-base-4f	1.0x	24	nus-det	0.714
R101	Pano-xl-4f	1.0x	24	nus-det	0.737

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{wang2023panoocc,
  title={PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation},
  author={Wang, Yuqi and Chen, Yuntao and Liao, Xingyu and Fan, Lue and Zhang, Zhaoxiang},
  journal={arXiv preprint arXiv:2306.10013},
  year={2023}
}

Acknowledgement

Many thanks to the following open-source projects:

SeaBird-Go/PanoOcc