Open-source alternatives to Polyscope

Potree is a web-based point cloud rendering engine and viewer designed for the visualization and analysis of massive 3D spatial datasets and LIDAR scans. It functions as a geospatial analysis tool that enables the interactive exploration of high-density point clouds directly within a web browser using WebGL. The system utilizes eye-dome lighting to enhance depth perception of 3D structures and supports virtual reality for immersive spatial exploration. It provides specialized capabilities for 3D scene documentation through hierarchical annotations and the creation of animated camera fly-throu

Entwine - point cloud organization for massive datasets

The Point Cloud Library is a collection of C++ algorithms designed for filtering, registering, and analyzing large-scale 3D spatial datasets. It provides a framework for 3D point cloud processing, incorporating tools for spatial data filtering and geometric feature estimation. The library includes specialized systems for aligning multiple spatial datasets into a single unified coordinate system and a rendering engine for the visual inspection and analysis of processed point cloud data. It also features tools for calculating spatial descriptors to identify structural patterns and shapes within

FAST_LIO is a real-time SLAM system and LiDAR-inertial odometry package designed for simultaneous localization and mapping. It functions as a state estimation engine and 3D mapping tool that fuses LiDAR point clouds with inertial measurement unit data to provide robust robot state estimation. The system utilizes a tightly-coupled sensor fusion approach with an iterative Kalman filter to estimate position and orientation. It distinguishes itself through direct point-to-plane matching, which calculates odometry by matching raw lidar points to the map surface without manual geometric feature ext

CGAL is a software library that provides a comprehensive collection of computational geometry algorithms and data structures. It is built around a geometry kernel that defines fundamental geometric primitives and operations, enabling the construction of complex geometric objects and the computation of geometric predicates with exact arithmetic for reliable results. The library covers a wide range of geometric computation capabilities, including the construction of convex hulls, triangulations of point sets, and the generation of Voronoi diagrams. It also supports the processing of polygonal m

The Intel RealSense SDK is a software development kit providing drivers and libraries for interfacing with depth cameras to capture color, depth, and infrared data streams. It includes a depth camera driver for device discovery and sensor configuration, a stereo vision library for computing depth maps and aligning frames, and a 3D point cloud generator to transform depth and infrared frames into spatial representations. The SDK distinguishes itself through on-chip depth calculation and stereo calibration, using internal vision processors to reduce host CPU load. It supports hardware-level str

Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove

mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing convolutional neural networks, a toolkit for image and video preprocessing, and a collection of high-performance deep learning vision operators. The project is distinguished by its hardware-accelerated kernels for complex operations such as deformable convolutions and region pooling. It features a configuration-driven framework that allows for the dynamic instantiation of network layers and the registration of custom modules without modifying code. The library covers a broad

LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto

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

efficient tools for LiDAR processing

Polygonal Surface Reconstruction from Point Clouds (C++ & Python)

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

Robotics with GPU computing

Pytorch framework for doing deep learning on point clouds.

A project demonstrating how to use the libs of cuPCL.

Kaolin is a PyTorch 3D deep learning library providing a comprehensive suite of tools for 3D geometry processing, physics simulation, data visualization, and gradient-based rendering for computer vision. The library includes a differentiable 3D renderer and a geometry processing toolkit for converting and transforming 3D representations such as meshes and point clouds. It also features a 3D physics simulation engine to calculate physical interactions and collisions between three-dimensional objects and scenes. The toolkit provides utilities for 3D data visualization, including the creation o

An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Contains the main OctoMap library, the viewer octovis, and dynamicEDT3D.

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud processing framework designed to develop, train, and evaluate machine learning models that identify and locate objects in three dimensional space. The project includes a GPU-accelerated geometry engine for high-performance implementation of 3D intersection over union and rotated non-maximum suppression. It also provides a distributed model training tool to scale the training and testing of detection models across multiple GPUs and computing nodes. The framework covers point c

PDAL is Point Data Abstraction Library. GDAL for point cloud data.

World's first general purpose 3D object detection codebse.

Create multi res point cloud to use with potree

A LiDAR odometry pipeline that just works

Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving