In this paper, we propose a novel network, SVDFormer, to tackle two specific
challenges in point cloud completion: understanding faithful global shapes from
incomplete point clouds and generating high-accuracy local structures. Current
methods either perceive shape patterns using only 3D coordinates or import
extra images with well-calibrated intrinsic parameters to guide the geometry
estimation of the missing parts. However, these approaches do not always fully
leverage the cross-modal self-structures available for accurate and
high-quality point cloud completion. To this end, we first design a Self-view
Fusion Network that leverages multiple-view depth image information to observe
incomplete self-shape and generate a compact global shape. To reveal highly
detailed structures, we then introduce a refinement module, called
Self-structure Dual-generator, in which we incorporate learned shape priors and
geometric self-similarities for producing new points. By perceiving the
incompleteness of each point, the dual-path design disentangles refinement
strategies conditioned on the structural type of each point. SVDFormer absorbs
the wisdom of self-structures, avoiding any additional paired information such
as color images with precisely calibrated camera intrinsic parameters.
Comprehensive experiments indicate that our method achieves state-of-the-art
performance on widely-used benchmarks. Code will be available at
this https URL

本文介绍 SVDFormer，一种创新的网络用于解决点云完成中的两个具体挑战：从不完全的点云中理解忠实的整体形状和生成高精度的局部结构。