Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection

• URL: http://arxiv.org/abs/2408.00286v1
• Date: Thu, 1 Aug 2024 05:04:22 GMT
• Title: Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection
• Authors: Jiacheng Deng, Jiahao Lu, Tianzhu Zhang,
• Abstract summary: 3D object detection is essential for understanding 3D scenes. Recent developments in semi-supervised methods seek to mitigate this problem by employing a teacher-student framework to generate pseudo-labels for unlabeled point clouds. We introduce an Agent-based Diffusion Model for Semi-supervised 3D Object Detection (Diff3DETR)
• Score: 33.58208166717537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D object detection is essential for understanding 3D scenes. Contemporary techniques often require extensive annotated training data, yet obtaining point-wise annotations for point clouds is time-consuming and laborious. Recent developments in semi-supervised methods seek to mitigate this problem by employing a teacher-student framework to generate pseudo-labels for unlabeled point clouds. However, these pseudo-labels frequently suffer from insufficient diversity and inferior quality. To overcome these hurdles, we introduce an Agent-based Diffusion Model for Semi-supervised 3D Object Detection (Diff3DETR). Specifically, an agent-based object query generator is designed to produce object queries that effectively adapt to dynamic scenes while striking a balance between sampling locations and content embedding. Additionally, a box-aware denoising module utilizes the DDIM denoising process and the long-range attention in the transformer decoder to refine bounding boxes incrementally. Extensive experiments on ScanNet and SUN RGB-D datasets demonstrate that Diff3DETR outperforms state-of-the-art semi-supervised 3D object detection methods.

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