Point cloud completion referring to completing 3D shapes from partial 3D
point clouds is a fundamental problem for 3D point cloud analysis tasks.
Benefiting from the development of deep neural networks, researches on point
cloud completion have made great progress in recent years. However, the
explicit local region partition like kNNs involved in existing methods makes
them sensitive to the density distribution of point clouds. Moreover, it serves
limited receptive fields that prevent capturing features from long-range
context information. To solve the problems, we leverage the cross-attention and
self-attention mechanisms to design novel neural network for processing point
cloud in a per-point manner to eliminate kNNs. Two essential blocks Geometric
Details Perception (GDP) and Self-Feature Augment (SFA) are proposed to
establish the short-range and long-range structural relationships directly
among points in a simple yet effective way via attention mechanism. Then based
on GDP and SFA, we construct a new framework with popular encoder-decoder
architecture for point cloud completion. The proposed framework, namely
PointAttN, is simple, neat and effective, which can precisely capture the
structural information of 3D shapes and predict complete point clouds with
highly detailed geometries. Experimental results demonstrate that our PointAttN
outperforms state-of-the-art methods by a large margin on popular benchmarks
like Completion3D and PCN. Code is available at:
this https URL

利用跨注意力机制和自我关注机制设计新型神经网络 PointAttN，消除了现有方法中局部区域划分对点云密度分布敏感的问题，其在处理点云的过程中以点为基本单位进行计算，以简单而有效的方式直接建立点之间的短程和长程结构关系，因此可以精确地捕捉三维形状的结构信息，并预测具有高度详细几何形状的完整点云。PointAttN 在 Completion3D 和 PCN 等流行基准测试中优于现有技术的表现，代码可在 https URL 上获取。