We propose a novel, end-to-end trainable, deep network called ParSeNet that
decomposes a 3D point cloud into parametric surface patches, including B-spline
patches as well as basic geometric primitives. ParSeNet is trained on a
large-scale dataset of man-made 3D shapes and captures high-level semantic
priors for shape decomposition. It handles a much richer class of primitives
than prior work, and allows us to represent surfaces with higher fidelity. It
also produces repeatable and robust parametrizations of a surface compared to
purely geometric approaches. We present extensive experiments to validate our
approach against analytical and learning-based alternatives. Our source code is
publicly available at: this https URL

该研究提出了一种称为 ParSeNet 的新型端到端可训练深度网络，该网络将 3D 点云分解为参数化表面补丁，包括 B 样条补丁以及基本几何图元。

ParSeNet: 用于 3D 点云的参数曲面拟合网络

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baseline networks (e.g. from FCN). When we add our proposed
global feature, and a technique for learning normalization parameters, accuracy
increases consistently even over our improved versions of the baselines. Our
proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow
and PASCAL-Context with small additional computational cost over baselines, and
near current state-of-the-art performance on PASCAL VOC 2012 semantic
segmentation with a simple approach. Code is available at
this https URL .

本文提出一种简单的技术，将全局上下文添加到深度卷积网络中，以用于语义分割，有效提高网络性能，并在 SiftFlow 和 PASCAL-Context 数据集上实现了最新成果。