# DiffuBox: Refining 3D Object Detection with Point Diffusion

Source: [DiffuBox: Refining 3D Object Detection with Point Diffusion](<https://www.sota2.com/research/paper/68fa76161e209131cea6b564>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:47:33.330Z

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

Published: 2024-05-25T03:14:55.000Z

## 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}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.16034>)

[PDF](<https://arxiv.org/pdf/2405.16034.pdf>)