Point cloud processing is very challenging, as the diverse shapes formed by
irregular points are often indistinguishable. A thorough grasp of the elusive
shape requires sufficiently contextual semantic information, yet few works
devote to this. Here we propose DensePoint, a general architecture to learn
densely contextual representation for point cloud processing. Technically, it
extends regular grid CNN to irregular point configuration by generalizing a
convolution operator, which holds the permutation invariance of points, and
achieves efficient inductive learning of local patterns. Architecturally, it
finds inspiration from dense connection mode, to repeatedly aggregate
multi-level and multi-scale semantics in a deep hierarchy. As a result, densely
contextual information along with rich semantics, can be acquired by DensePoint
in an organic manner, making it highly effective. Extensive experiments on
challenging benchmarks across four tasks, as well as thorough model analysis,
verify DensePoint achieves the state of the arts.

提出了一种名为 DensePoint 的点云处理方法，通过推广卷积算子实现了针对不规则点配置的正则化网格卷积神经网络，并从密集连接模式中汲取灵感，在深层次结构中重复聚合多层次、多尺度语义信息，从而有机地获得具有密集上下文信息的多元语义，同时进行高效归纳学习，实验结果表明 DensePoint 方法优于现有方法，达到了业界最佳性能。