27 skills found
MeshInspector / MeshLibMesh processing library
cardwing / Codes For PVKDPoint-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)
wangx1996 / Lidar SegementationAn implementation on "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance" from IROS 2019
hzykent / VMNetImplementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation
yinjunbo / CenterPoint FusionThe proposed approach enhances the CenterPoint baseline with a multimodal fusion mechanism. First, inspired by PointPainting, an off-the-shelf Mask-RCNN model trained from nuImages is employed to generate 2D object mask information based on the camera images. Furthermore, the Cylinder3D is also adopted to produce the 3D semantic information of the input LiDAR point cloud. Then, an improved version of CenterPoint takes the painted points(with 2D instance segmentation and 3D semantic segmentation) as inputs for accurate object detection. Specifically, we replace the RPN module in CenterPoint with modified Spatial-Semantic Feature Aggregation(SSFA) to well address multi-class detection. A simple pseudo labeling technique is also integrated in a semi-supervised learning manner. In addition, the Test Time Augmentation(TTA) strategy including multiple flip and rotation operations is applied during the inference time. Finally, the detections generated from multiple voxel resolutions (0.05m to 0.125m) are assembled with 3D Weighted Bounding Box Fusion(WBF) technique to produce the final results.
MeshInspector / MeshInspectorMesh processing application
GuoPingPan / RPVNetThis job is Non-official PyTorch implementation of the Range-Point-Voxel Funsion Network for lidar point cloud segmentation.
YevgeniyEngineer / LiDAR Processing V2LiDAR processing ROS2. Segmentation: "Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process". Clustering: "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance".
hanskrupakar / 3D UNet PytorchAn implementation of 3D U-Net CNN models for the task of voxel-wise semantic segmentation of 3D MR images for isolation of Low-Grade and High Grade Gliomas, the common types of brain tumour.
ruanych / Opencv 3dPoint cloud related algorithm repository, developed based on OpenCV. Include Voxel Grid Filter Sampling, Random Sampling, Farthest Point Sampling (FPS), Total Least Squares Plane Estimate, Random Sample Consensus (RANSAC), Multi-plane Detection/Segmentation in Point Cloud...
SCUT-BIP-Lab / PointDC[ICCV 2023] PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering
morte2025 / CVC ROSAn ROS implementation for paper "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance"
limingado / NSCThe code is an implementation of the Nystrӧm-based spectral clustering with the K-nearest neighbour-based sampling (KNNS) method (Pang et al. 2021). It is aimed for individual tree segmentation using airborne LiDAR point cloud data. When using the code, please cite as: Yong Pang, Weiwei Wang, Liming Du, Zhongjun Zhang, Xiaojun Liang, Yongning Li, Zuyuan Wang (2021) Nystrӧm-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation, International Journal of Digital Earth Code files: ‘segmentation.py’: the main function, including deriving local maximum from Canopy Height Model (CHM); ‘VNSC.py’: other functions for the algorithm, including mean-shift voxelization, similarity graph construction, KNNS sampling, eigendecomposition, k-means clustering, as well as the computation and writing of individual tree parameters. Key parameters: When using the code, users can adjust the values of local maximum window, gap (the upper limit of the number of final clusters), knn (the number of k-nearest neighbours in the similarity graph) and quantile in meanshift method based specific data characteristics. Currently, the value of local maximum window is 3m ×3m, the value of gap is defined as the 1.5 times of the local maximum detected from CHM. Parameter knn can be defined as a constant value (40 in the code) based on the data characteristics, or be determined through the relationship between it and the number of voxels. The default setting of quantile in meanshift method is the average density of point clouds. More details can be found in Pang et al. (2021). Test data: ‘ALS_pointclouds.txt’: point cloud data; ‘ALS_CHM.tif’: CHM of the point cloud data; ‘Reference_tree.csv’: field measurements for algorithm validation. The position was measured using differential GNSS. The tree height of each tree in this file is obtained by regression estimation. Outputs: ‘Data_seg.csv’: coordinate of each point (x, y, z) as well as its cluster label after segmentation; ‘Parameter.csv’: individual tree parameters (TreeID, Position_X, Position_Y, Crown, Height) based on the calculation described in Pang et al. (2021).
vlkniaz / SSZCode for the paper: Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation
xianyuMeng / VV Net Voxel VAE Net With Group Convolutions For Point Cloud Segmentationofficial implementation for our ICCV 2019 paper : Voxel VAE Net with Group Convolutions for Point Cloud Segmentation
petteriTeikari / VesselNN DatasetOpen-source volumetric brain vasculature dataset — 12 two-photon microscopy stacks with dense voxel-level segmentation labels
DavidGillsjo / Bssc NetThis is a PyTorch implementation of a Bayesian Convolutional Neural Network (BCNN) for Semantic Scene Completion on the SUNCG dataset. Given a depth image the network outputs a semantic segmentation and entropy score in 3D voxel format.
wkentaro / Hrp2 Apc3D Object Segmentation for Shelf Bin Picking by Humanoid with Deep Learning and Occupancy Voxel Grid Map (Humanoids2016)
SmileJET / VCLIPSegOfficial Code for our MICCAI paper "VCLIPSeg: Voxel-Wise CLIP-Enhanced Model for Semi-supervised Medical Image Segmentation"
xmba15 / Curved Voxel ClusteringPointcloud Segmentation by Curved-Voxel Clustering
