Scene Flow Prior Based Point Cloud Completion with Masked Transformer (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v38i21.30434Keywords:
Point Cloud Completion, Scene Flow Estimation, Transformer ModelAbstract
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.Downloads
Published
2024-03-24
How to Cite
Ding, J., Que, Y., Zhang, J., & Wu, C. (2024). Scene Flow Prior Based Point Cloud Completion with Masked Transformer (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23473-23474. https://doi.org/10.1609/aaai.v38i21.30434
Issue
Section
AAAI Student Abstract and Poster Program