fixed a bug in octree_search which was semantically doing something bad: for eachradiusSearch/nearestKSearch/approxNearestSearchcall with a PointCloudConstPtr, the octree was getting recreated. Changed the API to be consistent with the rest of PCL (including pcl_search and pcl_kdtree) where we...
import point_cloud_utils as pcu # v is a #v by 3 NumPy array of vertices # f is an #f by 3 NumPy array of face indexes into v v, f = pcu.load_mesh_vfc("my_model.ply") # Compute principal min/max curvature magnitudes (k1, k2) and directions (d1, d2) # using the one ...
Open System for Earthquake Engineering Simulation in Python OLS: Ordinary least squares PC: Point Cloud PCA: Principal Component Analysis RAM: Random-access memory \(\hat{x}_{i}\) and \(\hat{x}_{j}\) : The means of the \(i_{th}\) and \(j_{th}\) dimensions of the data...
the intrinsic resolution of the point clouds is proposed as a normalizer to convert the mean square errors to PSNR numbers. In addition, the perceived local planes are investigated at different scales of the point cloud. As such, they
Segmentation of point cloud The segmentation of point cloud was conducted as Fig.6. After the preprocessing, the random sample consensus algorithm (RANSAC) was adopted to fit the sample stage plane29and separate the grain point clouds from the background. Then, based on curvature and normal angl...
(arXiv). The work adapts PointNet for local geometric properties (e.g. normal and curvature) estimation in noisy point clouds. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Zhou et al. from Apple (arXiv) This work studies 3D object detection using LiDAR point...
(arXiv). The work adapts PointNet for local geometric properties (e.g. normal and curvature) estimation in noisy point clouds. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Zhou et al. from Apple (arXiv) This work studies 3D object detection using LiDAR point...
(arXiv). The work adapts PointNet for local geometric properties (e.g. normal and curvature) estimation in noisy point clouds. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Zhou et al. from Apple (arXiv) This work studies 3D object detection using LiDAR point...
(arXiv). The work adapts PointNet for local geometric properties (e.g. normal and curvature) estimation in noisy point clouds. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Zhou et al. from Apple (arXiv) This work studies 3D object detection using LiDAR point...
(arXiv). The work adapts PointNet for local geometric properties (e.g. normal and curvature) estimation in noisy point clouds. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Zhou et al. from Apple (arXiv) This work studies 3D object detection using LiDAR point...