![]() ![]() We also present extensive analysis to understand how our diffusion model design affects performance in semi-supervised learning. We conduct experiments on the ScanNet and SUN RGB-D benchmark datasets to demonstrate that our approach achieves state-of-the-art performance against existing methods. Every box is composed of four parts (or areas. CSS determines the size, position, and properties (color, background, border size, etc.) of these boxes. Moreover, we integrate the diffusion model into the teacher-student framework, so that the denoised bounding boxes can be used to improve pseudo-label generation, as well as the entire semi-supervised learning process. When laying out a document, the browser's rendering engine represents each element as a rectangular box according to the standard CSS basic box model. Specifically, we include noises to produce corrupted 3D object size and class label distributions, and then utilize the diffusion model as a denoising process to obtain bounding box outputs. In this work, we propose Diffusion-SS3D, a new perspective of enhancing the quality of pseudo-labels via the diffusion model for semi-supervised 3D object detection. However, producing reliable pseudo-labels in a diverse 3D space still remains challenging. Existing methods typically employ a teacher-student framework with pseudo-labeling to leverage unlabeled point clouds. Download a PDF of the paper titled Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection, by Cheng-Ju Ho and 4 other authors Download PDF Abstract:Semi-supervised object detection is crucial for 3D scene understanding, efficiently addressing the limitation of acquiring large-scale 3D bounding box annotations. ![]()
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