point-cloud-processing

A list of papers and datasets about point cloud analysis (processing)

Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)

C++ library and programs for reading and writing ASPRS LAS format with LiDAR data

Papers, code and datasets about deep learning for 3D Object Detection.

Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Official implementation of "Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis", ICCV 2021

Implementations of a rather simple version of the Iterative Closest Point algorithm in various languages.

Probabilistic line extraction from 2-D range scan

Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions (RAL 2022)

PVT: Point-Voxel Transformer for 3D Deep Learning

Utilities for point cloud processing. read ply, write ply, search nearest neighbors using octree ...

Point cloud completion tool based on dictionary learning. Takes a PCL point cloud surface and fills in gaps or densifies sparse regions by learning from the various surface features of the cloud. This is done using a variation of the k-SVD dictionary learning algorithm that allows for continuous atoms and dealing with unstructured point cloud data. Written in Qt C++, using Eigen, OpenMP, and OpenGL.

Cross-platform library to communicate with LiDAR devices of the Blickfeld GmbH.

SDK for multi-agent collaborative perception.

Point cloud segmentation with Azure Kinect

Point TransformER - Paper Collection of Transformer based, Unsupervised and Self-supervised Point Cloud Understanding

Registration of human MRT point clouds

Sensor Fusion Nanodegree | Lidar Obstacle Detection in Autonomous Vehicles

Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction

In this project we detect, segment and track the obstacles of an ego car and its custom implementation of KDTree, obstacle detection, segmentation, clustering and tracking algorithm in C++ and compare it to the inbuilt algorithm functions of PCL library on a LiDAR's point cloud data.

Toolkit for Point Cloud Processing