This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).

The project consists of a program that classifies objects and collects their properties, using point cloud processing and neural networks.

Some fancy pointcloud completion model implemented in PyTorch

Seperate dual lidar lasers and load the intensity and ring-numbers for better control over lidar data. Using Argoverse dataset.

ifm O3R color pointcloud publisher ROS2

Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.

SPVD⚡: Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models (PyTorch Lightning)

A Python package for circle fitting.

Re-implementation of PCT with Pytorch-Lightning

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Dataset Annotation Checker

Velodyne VLP-16 LIDAR live point cloud viewer

Jakarto datasets containing real-world 3d data from lidar sensors.

Code for ``A Point Set Generation Network for 3D Object Reconstruction from a Single Image''

Code Release of PointSCNet: Point Cloud Structure and Correlation Learning based on Space Filling Curve guided Sampling

Dataset Generation Code for CVPR 2022 Paper Primtive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives

Mini AI training model predict....

This simulator is based on hte Plankton and used to test Vortex Robotics' AUV