3D point cloud is an important 3D representation for capturing real world 3D
objects. However, real-scanned 3D point clouds are often incomplete, and it is
important to recover complete point clouds for downstream applications. Most
existing point cloud completion methods use Chamfer Distance (CD) loss for
training. The CD loss estimates correspondences between two point clouds by
searching nearest neighbors, which does not capture the overall point density
distribution on the generated shape, and therefore likely leads to non-uniform
point cloud generation. To tackle this problem, we propose a novel Point
Diffusion-Refinement (PDR) paradigm for point cloud completion. PDR consists of
a Conditional Generation Network (CGNet) and a ReFinement Network (RFNet). The
CGNet uses a conditional generative model called the denoising diffusion
probabilistic model (DDPM) to generate a coarse completion conditioned on the
partial observation. DDPM establishes a one-to-one pointwise mapping between
the generated point cloud and the uniform ground truth, and then optimizes the
mean squared error loss to realize uniform generation. The RFNet refines the
coarse output of the CGNet and further improves quality of the completed point
cloud. Furthermore, we develop a novel dual-path architecture for both
networks. The architecture can (1) effectively and efficiently extract
multi-level features from partially observed point clouds to guide completion,
and (2) accurately manipulate spatial locations of 3D points to obtain smooth
surfaces and sharp details. Extensive experimental results on various benchmark
datasets show that our PDR paradigm outperforms previous state-of-the-art
methods for point cloud completion. Remarkably, with the help of the RFNet, we
can accelerate the iterative generation process of the DDPM by up to 50 times
without much performance drop.

本文提出了一种基于点扩散和精化的点云补全范式，其中条件生成网络使用去噪扩散概率模型生成粗糙补全， 精修网络进一步改进完成后的点云的质量，并开发了一个新的双向路径架构。 这种架构既能从部分观察到的点云中有效地提取多级特征以指导补全，也能准确地操纵三维点的空间位置以获得光滑的表面和清晰的细节。通过广泛的实验，证明了本文提出的方法在点云完成方面优于以往的最先进方法。值得注意的是，在精细网络的帮助下，我们可以将 DDPM 的迭代生成过程加速 50 倍，而不会影响其性能。