EC-Net：一种边缘感知的点集合并网络
EC-Net: an Edge-aware Point set Consolidation Network
摘要：
从3D扫描获得的点云通常是稀疏的、不规则的和有噪声的，并且需要被合并。在本文中，我们提出了第一个基于深度学习的边缘感知技术，以促进点云的整合。我们设计我们的网络来处理分组在本地补丁中的点，并训练它来学习和帮助巩固点，故意为边缘。为了实现这一点，我们制定了一个回归组件，同时恢复3D点坐标和点到边缘的距离从上采样的功能，和边缘感知的联合损失函数，直接最小化从输出点到3D网格和边缘的距离。与以前的基于神经网络的工作相比，我们的合并是边缘感知。在合成过程中，我们的网络可以处理检测到的尖锐边缘，并实现更准确的3D重建。此外，我们在虚拟扫描点云上训练了我们的网络，展示了我们的方法在合成和真实点云上的性能，展示了各种表面重建结果，并展示了我们的方法如何优于最先进的方法。
英文摘要：
Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is edge-aware. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
