计算机科学
点云
判别式
分割
噪音(视频)
扩散
概率逻辑
代表(政治)
点(几何)
过程(计算)
人工智能
模式识别(心理学)
数据挖掘
图像(数学)
数学
几何学
政治
政治学
法学
操作系统
热力学
物理
作者
Chang Liu,Aimin Jiang,Yibin Tang,Yanping Zhu,Qi Chen
标识
DOI:10.1109/icassp48485.2024.10447146
摘要
Point cloud segmentation plays a crucial role in extracting unique attributes and separating various objects, thereby enabling semantic comprehension and analysis. In this paper, we introduce a novel point cloud segmentation approach based on Diffusion Probabilistic Network (DDPM). The proposed model treats points as particles undergoing diffusion towards a noise distribution, and a reverse diffusion process transforms this noise distribution into the desired shape. Leveraging a Markov diffusion model in the reverse process enables generating point clouds with more refined and specific topological structures. After the diffusion step, multi-scale sampled features are fused to enhance the discriminative representation of 3D shapes. Objective and subjective experimental results demonstrate that our segmentation method outperforms state-of-the-art techniques in terms of evaluation metrics.
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