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。

Voint 云：用于三维理解的多视点云表示

Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding

Multi-view projection methods have demonstrated their ability to reach
state-of-the-art performance on 3D shape recognition. Those methods learn
different ways to aggregate information from multiple views. However, the
camera view-points for those views tend to be heuristically set and fixed for
all shapes. To circumvent the lack of dynamism of current multi-view methods,
we propose to learn those view-points. In particular, we introduce the
Multi-View Transformation Network (MVTN) that regresses optimal view-points for
3D shape recognition, building upon advances in differentiable rendering. As a
result, MVTN can be trained end-to-end along with any multi-view network for 3D
shape classification. We integrate MVTN in a novel adaptive multi-view pipeline
that can render either 3D meshes or point clouds. MVTN exhibits clear
performance gains in the tasks of 3D shape classification and 3D shape
retrieval without the need for extra training supervision. In these tasks, MVTN
achieves state-of-the-art performance on ModelNet40, ShapeNet Core55, and the
most recent and realistic ScanObjectNN dataset (up to 6% improvement).
Interestingly, we also show that MVTN can provide network robustness against
rotation and occlusion in the 3D domain. The code is available at
this https URL .

本文介绍了一种基于多角度变换的深度学习网络架构，通过改变视角，以使得机器在 3D 形状识别任务中取得更好的性能。该网络框架在静态视角方法的基础上，实现了自适应动态视角模式，将深度学习与可微渲染技术相结合，并在三个公开数据集上取得了较高的识别精度。