FLAT[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
Stars: ✭ 52 (+18.18%)
Mutual labels: point-cloud, 3d-perception
pointcloud viewerNo description or website provided.
Stars: ✭ 16 (-63.64%)
Mutual labels: point-cloud
Scan2Cap[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans
Stars: ✭ 81 (+84.09%)
Mutual labels: point-cloud
Cylinder3dRank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
Stars: ✭ 221 (+402.27%)
Mutual labels: point-cloud
SGGpoint[CVPR 2021] Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis (official pytorch implementation)
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Mutual labels: point-cloud
CgalThe public CGAL repository, see the README below
Stars: ✭ 2,825 (+6320.45%)
Mutual labels: point-cloud
superpose3dregister 3D point clouds using rotation, translation, and scale transformations.
Stars: ✭ 34 (-22.73%)
Mutual labels: point-cloud
Flownet3dFlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)
Stars: ✭ 249 (+465.91%)
Mutual labels: point-cloud
Spvnas[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Stars: ✭ 239 (+443.18%)
Mutual labels: point-cloud
CupochRobotics with GPU computing
Stars: ✭ 225 (+411.36%)
Mutual labels: point-cloud
Displaz.jlJulia bindings for the displaz lidar viewer
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Mutual labels: point-cloud
PointnetvladPointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018
Stars: ✭ 224 (+409.09%)
Mutual labels: point-cloud
SpareNetStyle-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)
Stars: ✭ 118 (+168.18%)
Mutual labels: point-cloud
Kitti DatasetVisualising LIDAR data from KITTI dataset.
Stars: ✭ 217 (+393.18%)
Mutual labels: point-cloud
PcnCode for PCN: Point Completion Network in 3DV'18 (Oral)
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Mutual labels: point-cloud
ldgcnnLinked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
Stars: ✭ 66 (+50%)
Mutual labels: point-cloud
3D Ground SegmentationA ground segmentation algorithm for 3D point clouds based on the work described in “Fast segmentation of 3D point clouds: a paradigm on LIDAR data for Autonomous Vehicle Applications”, D. Zermas, I. Izzat and N. Papanikolopoulos, 2017. Distinguish between road and non-road points. Road surface extraction. Plane fit ground filter
Stars: ✭ 55 (+25%)
Mutual labels: point-cloud
attMPTI[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation
Stars: ✭ 118 (+168.18%)
Mutual labels: point-cloud