Awesome GitHub Repositories点云处理

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework for manipulating point clouds and meshes through specialized algorithms designed for 3D data science workflows. The project includes a toolkit for 3D scene reconstruction to generate spatial models and align surfaces from raw data. It also functions as a GPU accelerated framework that offloads intensive spatial computations to the graphics processor to increase processing speed. The library covers a broad range of capabilities including physically based light simulations for vi

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

Draco is a library and toolset for compressing, transcoding, and decoding 3D geometric meshes and point cloud data. Its primary purpose is to reduce storage size and transmission bandwidth for 3D assets. The project includes a geometry optimizer specifically for glTF file containers to reduce asset footprints. It also features a hardened decoder designed to process malformed or untrusted 3D geometric data safely to prevent memory corruption and crashes. The software covers a broad range of 3D data processing capabilities, including geometric data reconstruction, point attribute management, a

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

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

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

PCDet is a LiDAR 3D object detection toolbox and point cloud processing library built on the PyTorch deep learning framework. It provides a system for identifying and locating three-dimensional objects within point cloud data. The project utilizes a data-model separation pattern to decouple dataset loading logic from the core detection pipeline. It features a multi-sensor fusion pipeline that combines data from multiple sensors into a shared spatial view and a distributed GPU training system to scale workloads across multiple graphics processors. The toolkit covers several capability areas,

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

Potree 是一个基于 Web 的点云渲染引擎和查看器，专为大规模 3D 空间数据集和 LIDAR 扫描的可视化与分析而设计。它作为一种地理空间分析工具，支持使用 WebGL 直接在 Web 浏览器中对高密度点云进行交互式探索。 该系统利用眼穹照明（eye-dome lighting）增强 3D 结构的深度感知，并支持虚拟现实以进行沉浸式空间探索。它通过分层注释和创建动画相机漫游导览，为 3D 场景文档提供专业功能。 该平台包括地理空间数据分析工具，如空间距离和面积测量、高程剖面分析，以及外部 shapefile 和 geopackage 的叠加。用户可以使用基于属性的过滤和裁剪体积隔离来提取特定特征，同时外部图像可以与点云视角对齐和同步。 Potree 采用预处理的二进制格式和基于八叉树的空间索引，以促进大规模数据集的异步数据流传输和细节层次（LOD）渲染。

PointNet 是一种深度学习架构，旨在直接处理和分类原始 3D 点云，无需体素化。它提供了一个 3D 对象分类系统、用于将点云划分为类别的语义分割框架，以及可视化 3D 形状的工具。 该项目利用变换网络将点云对齐到规范坐标空间，并采用基于对称函数的聚合方法，无论点序如何，都能将点级特征压缩为全局向量。它还具有多尺度分组架构，用于提取不同空间尺度的层级几何特征。 该系统包含用于将原始 3D 室内解析和形状数据转换为 HDF5 文件以进行训练的数据流水线。评估功能涵盖分割准确度指标，以及将分类错误的 3D 点云渲染为三视图图像以进行误差分析。

Kaolin 是一个 PyTorch 3D 深度学习库，提供了一套全面的工具，用于 3D 几何处理、物理模拟、数据可视化和用于计算机视觉的梯度渲染。 该库包括一个可微分的 3D 渲染器和一个用于转换和变换 3D 表示（如网格和点云）的几何处理工具包。它还具有一个 3D 物理模拟引擎，用于计算三维物体和场景之间的物理交互和碰撞。 该工具包提供用于 3D 数据可视化的实用工具，包括创建交互式视图和转盘动画。其他功能涵盖 3D 数据集管理、数据预处理和 3D 表示渲染。

该项目是一个基于 PyTorch 的深度学习模型框架，专为 3D 点云数据的分类和语义分割而设计。它提供了 PointNet 架构的实现，用于执行整个对象的全局类别标注以及大规模 3D 环境的详细分区。 该系统处理跨多个尺度的语义分割，范围从识别单个对象内的组件到标注大规模场景中的不同类别类型。 该框架包括用于处理无序点集的结构化组件，例如最远点采样、层级点集分组和对称集聚合网络。它还提供离线数据预处理工具，以及用于在外部软件中可视化分割输出的 3D 对象文件生成工具。

FAST_LIO 是一个实时 SLAM 系统和激光雷达惯性里程计包，专为同步定位与建图而设计。它作为状态估计引擎和 3D 建图工具，将激光雷达点云与惯性测量单元（IMU）数据融合，以提供稳健的机器人状态估计。 该系统利用紧耦合传感器融合方法和迭代卡尔曼滤波器来估计位置和方向。它通过直接点对面匹配脱颖而出，该匹配通过将原始激光雷达点与地图表面匹配来计算里程计，而无需手动提取几何特征。为了保持高处理速度，它采用了增量 KD 树建图和并行空间搜索树。 该框架涵盖了广泛的功能，包括用于校正空间畸变的运动去畸变和传感器时间戳同步。它还提供了用于传感器外参标定、传感器对齐初始化以及累积全局点云导出的实用程序。 该项目使用 C++ 实现，并提供用于集成外部 IMU 和激光雷达传感器数据流的接口。

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

CloudCompare 是一款用于处理和分析 3D 点云和多边形网格的专业软件。它作为一个 3D 网格分析工具和大型数据集可视化工具，旨在在 3D 环境中显示和管理数百万个点。 该软件提供点云比较的专业功能，利用优化的八叉树结构计算两个 3D 数据集之间的空间差异。这使得识别点云之间或点云与网格之间的变化和误差成为可能。 该系统涵盖了广泛的 3D 数据分析领域，包括大规模点云的处理和 3D 网格的几何评估。它通过内存高效的处理和针对海量数据集的专业渲染来保持高性能。

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

A LiDAR odometry pipeline that just works

A C++ & Python viewer for 3D data like meshes and point clouds