Scene Flow Prior Based Point Cloud Completion with Masked Transformer (Student Abstract)

It is necessary to explore an effective point cloud completion mechanism that is of great significance for real-world tasks such as autonomous driving, robotics applications, and multi-target tracking. In this paper, we propose a point cloud completion method using a self-supervised transformer model based on the contextual constraints of scene flow. Our method uses the multi-frame point cloud context relationship as a guide to generate a series of token proposals, this priori condition ensures the stability of the point cloud completion. The experimental results show that the method proposed in this paper achieves high accuracy and good stability.

Authors

• Junzhe Ding School of Rail Transportation, Soochow University
• Yufei Que School of Rail Transportation, Soochow University
• Jin Zhang
• Cheng Wu Tsinghua University

Keywords

• Point Cloud Completion
• Scene Flow Estimation
• Transformer Model