3D Point Cloud labeling tool
complete solution for LiDAR annotation with photo context
complete solution for LiDAR annotation with photo context
complete solution for LiDAR episodes annotation with photo context
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Import pointclouds in PCD format without annotations
Export only labeled items and prepares downloadable tar archive
Export pointclouds project and prepares downloadable tar archive
Import Pointcloud Episodes with Annotations and Photo context
Import pointclouds without annotations in .ply format from Team Files
Export project or dataset in Supervisely pointcloud episode format
Run 3D Detection and tracking algorithm on pointclouds or pointcloud episodes project
Converts Supervisely Pointcloud format to KITTI 3D
Import Point Cloud Project with Annotations and Photo context in Supervisely format
Deploy model as REST API service
Split one or multiple datasets into parts
Converts KITTI 3D format to Supervisely pointcloud format
Dashboard to configure, start and monitor training
Export items after the passing labeling job review
Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool
Deploy interpolation method as REST API service
Creates sequence of connected point clouds with tracklets
Import Pointcloud Episodes from KITTI-360 format
Export Pointclouds and Pointcloud episodes to ROS Bag format
Deploy MBPTrack as REST API service
Detailed statistics for all classes in pointcloud or episodes project
Train MMDetection3D for detection on Point Clouds data
Deploy MMDetection3D models to detect objects in Point Clouds
Create a new empty project with a meta of original project
30 pointclouds without annotations
30 pointclouds with annotations
Demo project with pointcloud episodes from LYFT 3D dataset without labels
Demo project with pointcloud episodes from KITTI dataset with labels
Demo project with pointcloud episodes from LYFT 3D dataset with labels
Demo project with pointcloud episodes from KITTI dataset without labels