First, the normal vectors of the point cloud are initially estimated by principal component analysis (PCA). Next, the neighborhood of points from different patches, which are close to the sharp feature, are mapped to a unit Gauss sphere, and the point cloud on the unit sphere is clustered....
In general, because there is no mathematical way to solve for the sign of the normal, its orientation computed via Principal Component Analysis (PCA) as shown above isambiguous, and not consistently oriented over an entire point cloud dataset. The figure below presents these effects on two secti...
无法直接用深度卷积网络对点云进行处理——the irregular domain and lack of ordering,提出一种新的基于transformer(源于自然语言处理)的framework——Point Cloud Transformer (PCT)进行点云学习。具有处理点序列的inherently permutation invariant,well-suited for point cloud learning。为了更好的capture local context,...
These points clouds will be triangulated and used for visualization in combination with surface normals estimated by geometrical approaches. However, the quality of the reconstruction depends on the density of the point cloud and the estimation of the surface normals. In this paper, we present a ...
Point cloud processing is a highly challenging task in 3D vision because it is unstructured and unordered. Recently, deep learning has been proven to be quite successful in point cloud recognition, registration, segmentation, etc. Neighborhood search operation is an important component of point cloud...
such as PU-Net and PointNet++, for down-stream point cloud analysis. Our method improves the state-of-the-art on multiple representative tasks that can benefit from understandings of the underlying surface topology, including point upsampling, normal estimation, mesh reconstruction and non-rigid sha...
within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation ...
点云数据cloudpointstanford斯坦福pcd Estimating Surface Normals in Noisy Point Cloud Data Niloy J. Mitra, An Nguyen Stanford University Symposium on Computational Geometry Normal Estimation for Noisy PCD The Normal Estimation Problem Given Noisy PCD sampled from a curve/surface Symposium on Computational ...
法向量估计法向量重定向The three-dimensional point cloudnormal estimationnormal vector redirection近年来,随着三维点云采集设备的发展,低成本获取被测物体更多细节的... 刘正 - 华北电力大学 被引量: 0发表: 2016年 加载更多研究点推荐 三维点云点法向量估计 方向一致性判断 离判断 云点法向量估计 点云 0关于...
作者提出了一种新的用于点云学习的框架,Point Cloud Transformer(PCT)。PCT是基于Transformer的,具有处理一系列点的置换不变性,非常适合点云学习。为了更好地捕获点云内的局部环境,作者加强了输入嵌入,支持最远点采样和最近邻搜索。该算法在形状分类、零件分割、语义分割和一般的估计任务中均取得了较好的性能。