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[ECCV'24] CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction

Zhangchen Ye1*, Tao Jiang1,2*, Chenfeng Xu3, Yiming Li4, and Hang Zhao1,2,5✉

1IIIS, Tsinghua University 2Shanghai AI Lab 3UC Berkeley 4New York University 5Shanghai Qi Zhi Institute

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News

[2024/09/23]: The preprint version is available on arXiv.
[2024/07/29]: Code Released.
[2024/07/04]: Our paper has been accepted by ECCV2024.

Abstract

Vision-based 3D occupancy prediction is significantly challenged by the inherent limitations of monocular vision in depth estimation. This paper introduces CVT-Occ, a novel approach that leverages temporal fusion through the geometric correspondence of voxels over time to improve the accuracy of 3D occupancy predictions. By sampling points along the line of sight of each voxel and integrating the features of these points from historical frames, we construct a cost volume feature map that refines current volume features for improved prediction outcomes. Our method takes advantage of parallax cues from historical observations and employs a data-driven approach to learn the cost volume. We validate the effectiveness of CVT-Occ through rigorous experiments on the Occ3D-Waymo dataset, where it outperforms state-of-the-art methods in 3D occupancy prediction with minimal additional computational cost.

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Model Zoo

All model weights can be download from <img src="https://ssl.gstatic.com/docs/doclist/images/drive_2022q3_32dp.png" alt="Google Drive" width="18"/> HERE

Occ3D-Waymo

Method	mIoU	Go	Vehicle	Pedestrian	Sign	Bicyclist	Traffic Light	Pole	Cons. Cone	Bicycle	Building	Vegetation	Tree Trunk	Road	Walkable
BEVFormer-w/o TSA	23.87	7.50	34.54	21.07	9.69	20.96	11.48	11.48	14.06	14.51	23.14	21.82	8.57	78.45	56.89
BEVFormer	24.58	7.18	36.06	21.00	9.76	20.23	12.61	14.52	14.70	16.06	23.98	22.50	9.39	79.11	57.04
SOLOFusion	24.73	4.97	32.45	18.28	10.33	17.14	8.07	17.83	16.23	19.30	31.49	28.98	16.93	70.95	53.28
BEVFormer-WrapConcat	25.07	6.20	36.17	20.95	9.56	20.58	12.82	16.24	14.31	16.78	25.14	23.56	12.81	79.04	56.83
CVT-Occ (ours)	27.37	7.44	41.00	23.93	11.92	20.81	12.07	18.03	16.88	21.37	29.40	27.42	14.67	79.12	59.09

Occ3D-NuScenes

Method	mIoU	others	barrier	bicycle	bus	car	Cons. vehicle	motorcycle	pedestrian	traffic cone	trailer	truck	Dri. Sur	other flat	sidewalk	terrain	manmade	vegetation
BEVFormer-w/o TSA	38.05	9.11	45.68	22.61	46.19	52.97	20.27	26.5	26.8	26.21	32.29	37.58	80.5	40.6	49.93	52.48	41.59	35.51
BEVFormer	39.04	9.57	47.13	22.52	47.61	54.14	20.39	26.44	28.12	27.46	34.53	39.69	81.44	41.14	50.79	54.00	43.08	35.60
CVT-Occ (ours)	40.34	9.45	49.46	23.57	49.18	55.63	23.1	27.85	28.88	29.07	34.97	40.98	81.44	40.92	51.37	54.25	45.94	39.71

Acknowledgement

Thanks to these excellent open source projects:

Citation

If you find our work useful for your research, please consider citing the paper:

@article{ye2024cvtocc,
  title={CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction},
  author={Ye, Zhangchen and Jiang, Tao and Xu, Chenfeng and Li, Yiming and Zhao, Hang},
  journal={arXiv preprint arXiv:2409.13430},
  year={2024},
  url={https://arxiv.org/abs/2409.13430}
}