Dig into Detailed Structures: Key Context Encoding and Semantic-based Decoding for Point Cloud Completion

Recovering the complete shape of a 3D object from limited viewpoints plays an important role in 3D vision. Recent point cloud completion methods prefer an encoding-decoding architecture for generating the global structure and local geometry from a set of input point proxies. In this paper, we introduce an innovative completion method aimed at uncovering structural details from input point clouds and maximizing their utility. Specifically, we improve both Encoding and Decoding for this task: (1) Key Context Fusion Encoding extracts and aggregates homologous key context by adaptively increasing the sampling bias towards salient structure and special contour points. (2) Semantic-based Decoding introduces a semantic EdgeConv module to prompt next Transformer decoder, which effectively learns and generates local geometry with semantic correlations from non-nearest neighbors. The experiments are evaluated on several 3D point cloud and 2.5D depth image datasets. Both qualitative and quantitative evaluations demonstrate that our method outperforms previous state-of-the-art methods.