  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.
