Displaying 1 to 20 from 69 results

draco - Draco is a library for compressing and decompressing 3D geometric meshes and point clouds

  •    C++

Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.Draco was designed and built for compression efficiency and speed. The code supports compressing points, connectivity information, texture coordinates, color information, normals, and any other generic attributes associated with geometry. With Draco, applications using 3D graphics can be significantly smaller without compromising visual fidelity. For users, this means apps can now be downloaded faster, 3D graphics in the browser can load quicker, and VR and AR scenes can now be transmitted with a fraction of the bandwidth and rendered quickly.

Open3D - Open3D: A Modern Library for 3D Data Processing

  •    C++

Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. We welcome contributions from the open-source community. Please cite our work if you use Open3D.

3D-Machine-Learning - A resource repository for 3D machine learning


In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. This repo is derived from my study notes and will be used as a place for triaging new research papers. To contribute to this Repo, you may add content through pull requests or open an issue to let me know.

pointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

  •    Python

Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.

pcl - Point Cloud Library (PCL)

  •    C++

The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. PCL is released under the terms of the BSD license, and thus free for commercial and research use. We are financially supported by a consortium of commercial companies, with our own non-profit organization, Open Perception. We would also like to thank individual donors and contributors that have been helping the project.

mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.

  •    Python

News: We released the codebase v0.14.0. In the recent nuScenes 3D detection challenge of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.

OpenPCDet - OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

  •    Python

OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. It is also the official code release of [PointRCNN], [Part-A^2 net] and [PV-RCNN].

torch-points3d - Pytorch framework for doing deep learning on point clouds.

  •    Python

This is a framework for running common deep learning models for point cloud analysis tasks against classic benchmark. It heavily relies on Pytorch Geometric and Facebook Hydra. The framework allows lean and yet complex model to be built with minimum effort and great reproducibility. It also provide a high level API to democratize deep learning on pointclouds. See our paper at 3DV for an overview of the framework capacities and benchmarks of state-of-the-art networks.

Det3D - A general 3D object detection codebse.

  •    Python

A general 3D Object Detection codebase in PyTorch. Please refer to INSTALATION.md.

awesome-robotic-tooling - Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace: https://freerobotics


To stop reinventing the wheel you need to know about the wheel. This list is an attempt to show the variety of open and free tools in software and hardware development, which are useful in professional robotic development. Your contribution is necessary to keep this list alive, increase the quality and to expand it. You can read more about it's origin and how you can participate in the contribution guide and related blog post. All new project entries will have a tweet from protontypes.

depth_clustering - :taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor

  •    C++

This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velodyne sensors, i.e. 16, 32 and 64 beam ones. I recommend using a virtual environment in your catkin workspace (<catkin_ws> in this readme) and will assume that you have it set up throughout this readme. Please update your commands accordingly if needed. I will be using pipenv that you can install with pip.

3d-tiles - Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:

  •    Batchfile

A building CAD model is fused with photogrammetry data using 3D Tiles, data courtesy of Bentley Systems. 3D Tiles is an open specification for sharing, visualizing, fusing, and interacting with massive heterogenous 3D geospatial content across desktop, web, and mobile applications.

lidar_camera_calibration - ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

  •    C++

The package is used to calibrate a LiDAR (config to support Hesai and Velodyne hardware) with a camera (works for both monocular and stereo). The package finds a rotation and translation that transform all the points in the LiDAR frame to the (monocular) camera frame. Please see Usage for a video tutorial. The lidar_camera_calibration/pointcloud_fusion provides a script to fuse point clouds obtained from two stereo cameras. Both of which were extrinsically calibrated using a LiDAR and lidar_camera_calibration. We show the accuracy of the proposed pipeline by fusing point clouds, with near perfection, from multiple cameras kept in various positions. See Fusion using lidar_camera_calibration for results of the point cloud fusion (videos).

pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

  •    Python

Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.

Easy3D - A lightweight, easy-to-use, and efficient C++ library for processing and rendering 3D data

  •    C++

Efficient data structures for representing and managing 3D models such as point clouds, polygonal surfaces (e.g., triangle meshes), polyhedral volumes (e.g., tetrahedral meshes), and graphs. Easy to add/access arbitrary types of per-element properties. Non-manifoldness is automatically resolved when loading models from files ... A set of widely used algorithms, e.g., point cloud normal estimation/re-orientation, Poisson surface reconstruction, RANSAC, mesh simplification, subdivision, smoothing, parameterization, remeshing, and more (the implementation of several surface mesh processing algorithms were taken from PMP).

superpoint_graph - Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

  •    Python

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning https://arxiv.org/pdf/1904.02113. To switch to the stable branch with only SPG, switch to release.

bpy - blender python scripts

  •    Python

Display, edit, filter, render, convert, generate and export colored point cloud PLY files. Works with any PLY file with 'x, y, z, nx, ny, nz, red, green, blue' vertex values. Vertex normals and colors are optional.

cilantro - A lean C++ library for working with point cloud data

  •    C++

cilantro is a lean and fast C++ library for working with point cloud data, with emphasis given to the 3D case. It includes efficient implementations for a variety of common operations, providing a clean API and attempting to minimize the amount of boilerplate code. The library is extensively templated, enabling operations on data of arbitrary numerical type and dimensionality (where applicable) and featuring a modular/extensible design of the more complex procedures. At the same time, convenience aliases/wrappers for the most common cases are provided. A high-level description of cilantro can be found in our technical report. Documentation (readthedocs.io, Doxygen API reference) is a work in progress. The short provided examples (built by default) cover a significant part of the library's functionality. Most of them expect a single command-line argument (path to a point cloud file in PLY format). One such input is bundled in examples/test_clouds for quick testing.

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