ImgAdaPoinTr: Improving Point Cloud Completion via Images and Segmentation.

Abstract

Point cloud completion is an essential task consisting of inferring and filling in missing parts of a 3D point cloud representation. In this paper, we present an ImgAdaPoinTr model, which extends the original Transformer encoder-decoder architecture by accurately incorporating visual information. Besides, we assumed using segmentation of 3D objects as a part of the pipeline due to acquiring an additional increase in performance. We also introduce the novel ImgPCN dataset generated by our rendering tool. The results show that our approach outperforms AdaPoinTr by average 2.9% and 10.3% in terms of Chamfer-Distance L1 and L2 metrics, respectively. The code and dataset are available via the link https://github.com/ImgAdaPoinTr.