DiffuBox: Refining 3D Object Detection with Point Diffusion

• URL: http://arxiv.org/abs/2405.16034v2
• Date: Sat, 07 Dec 2024 00:25:33 GMT
• Title: DiffuBox: Refining 3D Object Detection with Point Diffusion
• Authors: Xiangyu Chen, Zhenzhen Liu, Katie Z Luo, Siddhartha Datta, Adhitya Polavaram, Yan Wang, Yurong You, Boyi Li, Marco Pavone, Wei-Lun Chao, Mark Campbell, Bharath Hariharan, Kilian Q. Weinberger,
• Abstract summary: We introduce a novel diffusion-based box refinement approach to ensure robust 3D object detection and localization. This method employs a domain-agnostic diffusion model conditioned on the LiDAR points surrounding a coarse bounding box, to simultaneously refine the box's location, size, and orientation. Our results reveal significant improvements across different datasets, object classes and detectors.
• Score: 74.01759893280774
• License:
• Abstract: Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this challenge, we introduce a novel diffusion-based box refinement approach. This method employs a domain-agnostic diffusion model, conditioned on the LiDAR points surrounding a coarse bounding box, to simultaneously refine the box's location, size, and orientation. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets, object classes and detectors. Our PyTorch implementation is available at \href{https://github.com/cxy1997/DiffuBox}{https://github.com/cxy1997/DiffuBox}.

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