NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer.

Self-attention networks have revolutionized the field of natural language processing and have also made impressive progress in image analysis tasks. Corrnet3D proposes the idea of first obtaining the point cloud correspondence in point cloud registration. Inspired by these successes, we propose an unsupervised network for non-rigid point cloud registration, namely NrtNet, which is the first network using a transformer for unsupervised large deformation non-rigid point cloud registration.

An Improved Mixture Density Network for 3D Human Pose Estimation with Ordinal Ranking.

Estimating accurate 3D human poses from 2D images remains a challenge due to the lack of explicit depth information in 2D data. This paper proposes an improved mixture density network for 3D human pose estimation called the Locally Connected Mixture Density Network (LCMDN). Instead of conducting direct coordinate regression or providing unimodal estimates per joint, our approach predicts multiple possible hypotheses by the Mixture Density Network (MDN).