Multi-view projection methods have demonstrated promising performance on 3D
understanding tasks like 3D classification and segmentation. However, it
remains unclear how to combine such multi-view methods with the widely
available 3D point clouds. Previous methods use unlearned heuristics to combine
features at the point level. To this end, we introduce the concept of the
multi-view point cloud (Voint cloud), representing each 3D point as a set of
features extracted from several view-points. This novel 3D Voint cloud
representation combines the compactness of 3D point cloud representation with
the natural view-awareness of multi-view representation. Naturally, we can
equip this new representation with convolutional and pooling operations. We
deploy a Voint neural network (VointNet) to learn representations in the Voint
space. Our novel representation achieves \sota performance on 3D
classification, shape retrieval, and robust 3D part segmentation on standard
benchmarks ( ScanObjectNN, ShapeNet Core55, and ShapeNet Parts).

本文介绍了一种名为 Voint Cloud 的新型三维点云表示方法，该方法将多视角方法的视角感知能力与三维点云紧凑性相结合，利用 VointNet 进行学习，取得了在各项基准测试上的最佳表现。