# Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection Source: [Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:06:43.001Z Authors: Jiacheng Deng, Jiahao Lu, Tianzhu Zhang Published: 2024-08-01T05:04:22.000Z ## 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. ## 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. - [3D Object Detection on ScanNet V2 (val)](): 3D Object Detection; ScanNet V2 (val); mAP@0.25: 45.1. ## Original sources and implementation [Original paper]() [PDF]()