3D landmark detection plays a pivotal role in various applications such as 3D registration, pose estimation, and virtual try-on. While considerable success has been achieved in 2D human landmark detection or pose estimation, there is a notable scarcity of reported works on landmark detection in unordered 3D point clouds. This paper introduces a novel challenge, namely 3D landmark detection on human point clouds, presenting two primary contributions. Firstly, we establish a comprehensive human point cloud dataset, named HPoint103, designed to support the 3D landmark detection community. This dataset comprises 103 human point clouds created with commercial software and actors, each manually annotated with 11 stable landmarks. Secondly, we propose a Dual Cascade Point Transformer (D-CPT) model for precise point-based landmark detection. D-CPT gradually refines the landmarks through cascade Transformer decoder layers across the entire point cloud stream, simultaneously enhancing landmark coordinates with a RefineNet over local regions. Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT. Additionally, the integration of our RefineNet into existing methods consistently improves performance.

本篇论文介绍了一种在人体点云上进行三维关键点检测的挑战，并提出了一种名为Dual Cascade Point Transformer (D-CPT)的模型，该模型通过级联的Transformer解码器逐步优化关键点，在本地区域使用RefineNet增强关键点坐标，通过在HPoint103和公共数据集DHP19上进行对比评估，验证了我们的D-CPT模型在关键点检测方面的显著性能优势，并表明将我们的RefineNet集成到现有方法中可以持续提高性能。

基于人体点云的三维特征点检测：一个基准和双级级联点变换器框架