Diffusion-based 3D Object Detection with Random Boxes

• URL: http://arxiv.org/abs/2309.02049v1
• Date: Tue, 5 Sep 2023 08:49:53 GMT
• Title: Diffusion-based 3D Object Detection with Random Boxes
• Authors: Xin Zhou, Jinghua Hou, Tingting Yao, Dingkang Liang, Zhe Liu, Zhikang Zou, Xiaoqing Ye, Jianwei Cheng, Xiang Bai
• Abstract summary: Existing anchor-based 3D detection methods rely on empiricals setting of anchors, which makes the algorithms lack elegance. Our proposed Diff3Det migrates the diffusion model to proposal generation for 3D object detection by considering the detection boxes as generative targets. In the inference stage, the model progressively refines a set of random boxes to the prediction results.
• Score: 58.43022365393569
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the algorithms lack elegance. In recent years, we have witnessed the rise of several generative models, among which diffusion models show great potential for learning the transformation of two distributions. Our proposed Diff3Det migrates the diffusion model to proposal generation for 3D object detection by considering the detection boxes as generative targets. During training, the object boxes diffuse from the ground truth boxes to the Gaussian distribution, and the decoder learns to reverse this noise process. In the inference stage, the model progressively refines a set of random boxes to the prediction results. We provide detailed experiments on the KITTI benchmark and achieve promising performance compared to classical anchor-based 3D detection methods.

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