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About this paper
Xiangyu Chen, Zhenzhen Liu, Katie Z Luo, Siddhartha Datta, Adhitya Polavaram, Yan Wang, Yurong You, Boyi Li and 5 more
DiffuBox presents a diffusion-based post-processing method for refining 3D object detection bounding boxes by learning object shapes in a Normalized Box View (NBV). This approach consistently enhances localization accuracy and generalizes across various detectors and domain shifts, yielding mAP gains up to 23.49 for car detection in unseen environments.
Key insights
- The relative distribution of LiDAR points with respect to their corresponding ground-truth bounding boxes remains surprisingly consistent across different domains, despite absolute object size variations.
- Decoupling object shape (via NBV) from absolute scale and position allows for a domain-agnostic refinement process that directly addresses scale-related domain shifts.
- Diffusion models can effectively learn canonical object shapes from noisy point cloud representations, enabling them to 'denoise' and refine imprecise bounding box parameters.
Summary by alphaXiv.
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