GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2412.14579v1
• Date: Thu, 19 Dec 2024 06:57:37 GMT
• Title: GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting
• Authors: Qianpu Sun, Changyong Shu, Sifan Zhou, Zichen Yu, Yan Chen, Dawei Yang, Yuan Chun,
• Abstract summary: Previous weakly-supervised NeRF methods balance efficiency and accuracy, with mIoU varying by 5-10 points due to sampling count along camera rays. We propose GSRender, which naturally employs 3D Gaussian splatting for occupancy prediction, simplifying the sampling process. Our approach achieves SOTA results in RayIoU (+6.0), while narrowing the gap with 3D supervision methods.
• Score: 7.936178003928951
• License:
• Abstract: 3D occupancy perception is gaining increasing attention due to its capability to offer detailed and precise environment representations. Previous weakly-supervised NeRF methods balance efficiency and accuracy, with mIoU varying by 5-10 points due to sampling count along camera rays. Recently, real-time Gaussian splatting has gained widespread popularity in 3D reconstruction, and the occupancy prediction task can also be viewed as a reconstruction task. Consequently, we propose GSRender, which naturally employs 3D Gaussian Splatting for occupancy prediction, simplifying the sampling process. In addition, the limitations of 2D supervision result in duplicate predictions along the same camera ray. We implemented the Ray Compensation (RC) module, which mitigates this issue by compensating for features from adjacent frames. Finally, we redesigned the loss to eliminate the impact of dynamic objects from adjacent frames. Extensive experiments demonstrate that our approach achieves SOTA (state-of-the-art) results in RayIoU (+6.0), while narrowing the gap with 3D supervision methods. Our code will be released soon.

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