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 https://github.com/czvvd/SVDFormer.

翻译：本文提出一种新型网络SVDFormer，旨在解决点云补全中的两个特定挑战：从不完整点云中理解可靠的全局形状，以及生成高精度的局部结构。现有方法要么仅利用三维坐标感知形状模式，要么引入经过精确标定内参的额外图像来指导缺失部分的几何估计。然而，这些方法并未充分挖掘可用于实现精确高质量点云补全的跨模态自结构信息。为此，我们首先设计了一个自视图融合网络，利用多视图深度图像信息观测不完整自形状并生成紧凑的全局形状。为揭示高精细结构，我们引入一个名为自结构双生成器的细化模块，该模块融合了学习到的形状先验与几何自相似性以生成新点。通过感知每个点的残缺程度，双路径设计根据各点结构类型解耦细化策略。SVDFormer汲取自结构之智慧，无需任何额外配对信息（如经精确标定相机内参的彩色图像）。全面实验表明，本方法在广泛采用的基准测试中达到了最先进性能。代码将发布于 https://github.com/czvvd/SVDFormer。