Open Source Mac Point Cloud Software
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Point Cloud Software for Mac
View 6 business solutionsBrowse free open source Point Cloud software and projects for Mac below. Use the toggles on the left to filter open source Point Cloud software by OS, license, language, programming language, and project status.
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Intel RealSense
Intel® RealSense SDK
Intel® RealSense™ SDK 2.0 is a cross-platform library for Intel® RealSense™ depth cameras. The SDK allows depth and color streaming and provides intrinsic and extrinsic calibration information. The library also offers synthetic streams (point cloud, depth aligned to color and vise-versa), and built-in support for recording and playback of streaming sessions. Intel has EOLed the LiDAR, Facial Authentication, and Tracking product lines. These products have been discontinued and will no longer be available for new orders. Intel WILL continue to sell and support stereo products including the following: D410, D415, D430, , D401 ,D450 modules and D415, D435, D435i, D435f, D405, D455, D457 depth cameras. We will also continue the work to support and develop our LibRealSense open source SDK. -
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COLMAP
Structure-from-Motion and Multi-View Stereo
COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for the reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license. -
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CloudCompare
CloudCompare main repository
CloudCompare is a 3D point cloud (and triangular mesh) processing software. It was originally designed to perform comparison between two 3D points clouds (such as the ones obtained with a laser scanner) or between a point cloud and a triangular mesh. It relies on an octree structure that is highly optimized for this particular use-case. It was also meant to deal with huge point clouds (typically more than 10 million points, and up to 120 million with 2 GB of memory). -
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Point Cloud Library
A standalone, large scale, open project for 2D/3D image processing
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. Whether you’ve just discovered PCL or you’re a long time veteran, this page contains links to a set of resources that will help consolidate your knowledge on PCL and 3D processing. An additional Wiki resource for developers is available too. To simplify both usage and development, we split PCL into a series of modular libraries. PCL is cross-platform, and has been successfully compiled and deployed on Linux, MacOS, Windows, and Android. To simplify development, PCL is split into a series of smaller code libraries, that can be compiled separately. This modularity is important for distributing PCL on platforms with reduced computational or size constraints. -
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Point Cloud Library
Standalone, large scale, open project for 3D point cloud processing.
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 Open Perception, Willow Garage, NVIDIA, Google, Toyota, Trimble, Urban Robotics, Honda Research Institute, Sandia, Dinast, Optronic, Ocular Robotics, Velodyne, Fotonic, Leica Geosystems, and MKE. We would also like to thank individual donors and contributors that have been helping the project. -
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Discontinuity Set Extractor (DSE) is programmed by Adrián Riquelme for testing part of his PdD studies. Its aim is to extract discontinuity sets from a rock mass. The input data is a 3D point cloud, which can be acquired by means of a 3D laser scanner (LiDAR or TLS), digital photogrammetry techniques (such as SfM) or synthetic data. It applies a proposed methodology to semi-automatically identify points members of an unorganised 3D point cloud that are arranged in 3D space by planes.
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Point Cloud Skinner for Blender
Python script (addon) for Blender to skin point cloud (verts to face).
What is Point Cloud Skinner? This is a Python script for Blender 2.6x or later and allows you to create a surface from just a cloud of vertices. You can get a complete 3D mesh with faces on it out of just a point cloud that has only vertices and no faces. Please watch the video to get the idea of what the script can do for your artwork. Features: This script, Point Cloud Skinner can skin a cloud of vertices, which means to create a 3D surfaced mesh out of just a cloud of vertices that has no faces. It can skin any shapes of point clouds, such as a surveyed geography point cloud that represents a landform somewhere, a fluid volumetric point cloud obtained by numerical fluid simulation, or anything you like. You can see some sample meshes that the script produced in "Results" part below. -
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3DFOREST
tool for manage and process TLS lidar data from forest environment
Software tool for tree atributes extraction from point cloud data acquired by terrestrial laser scanner in forest enviroment. 3DForest is not only a visualizer af data but brings new and complex data management for foresters and researchers -
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Open source point cloud viewer forked from Radiohead House of Cards Project, based on the Processing Programming Language.
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Canopy Reconstruction
Software for the reconstruction of plants using multiple camera views
This software reconstructs 3D plant models in multiple views. Point cloud input is segmented, then patches of leaf surface are grown using the level set method. Please see the CPIB website for news of updates and further development. -
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Cloud is a point-cloud viewer for Mac OS X. Cloud reads a point-cloud data (e.g. x y z r g b) and renders it by using OpenGL.
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Dynamic Robot Localization
Point cloud registration pipeline for robot localization and 3D
The dynamic_robot_localization is a ROS package that offers 3 DoF and 6 DoF localization using PCL and allows dynamic map update using OctoMap. It's a modular localization pipeline, that can be configured using yaml files (detailed configuration layout available in drl_configs.yaml and examples of configurations available in guardian_config and dynamic_robot_localization_tests). Even though this package was developed for robot self-localization and mapping, it was implemented as a generic, configurable and extensible point cloud matching library, allowing its usage in related problems such as estimation of the 6 DoF pose of an object and 3D object scanning. -
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EkNN
Extracting k nearest neighbors for point cloud
Speed up kNN searching algorithm by extracting nearest neighbors diectly other than searching them one by one -
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Manifold mesh processing
Mesh and point cloud processing tools
A suite of tools for processing and analyzing meshes and point clouds. 1) Ensuring that meshes are manifold, ie, "nice" 2) Simple smoothing/noise reduction 3) Bare-bones point cloud to mesh and robust normals for point clouds 4) Mesh simplification 5) Curvature calculation on meshes 6) Local shape descriptors on meshes -
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Objectron
A dataset of short, object-centric video clips
The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. In addition, to ensure geo-diversity, our dataset is collected from 10 countries across five continents. Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras. -
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PhotoCloud is a tool to browse and a navigate for datasets composed of (a) a collection of registered images and (b) a 3D model over which images are aligned. The 3D model can be a point-cloud, triangle mesh. PhotoCloud is based on a client/server architecture and it is scalable: very large dataset (both in terms of (a) and (b) can be handled.
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using simple cuboids and gjk algorithm, this general purpose algorithm gives a ready made code to work with swissranger 3D camera.
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Various tools for point cloud processing. Similarity transformations, 2.5D incisions and Point Cloud coloring.
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Semantic Segmentation Editor
Web labeling tool for bitmap images and point clouds
A web-based labeling tool for creating AI training data sets (2D and 3D). The tool has been developed in the context of autonomous driving research. It supports images (.jpg or .png) and point clouds (.pcd). It is a Meteor app developed with React, Paper.js, and three.js. -
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XIVO
X Inertial-aided Visual Odometry
XIVO is an open-source repository for visual-inertial odometry/mapping. It is a simplified version of Corvis [Jones et al.,Tsotsos et al.], designed for pedagogical purposes, and incorporates odometry (relative motion of the sensor platform), local mapping (pose relative to a reference frame of the oldest visible features), and global mapping (pose relative to a global frame, including loop-closure and global re-localization, this feature, present in Corvis, is not yet incorporated in XIVO). XIVO runs at 140FPS on stored data (here from a RealSense D435i sensor) or on live streams with a latency of around 1-7ms, depending on the hardware. It takes as input video frames from a calibrated camera and inertial measurements from an IMU and outputs a sparse point cloud with attribute features and 6 DOF pose of the camera. It performs auto-calibration of the relative pose between the camera and the IMU as well as the time-stamp alignment. -
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ZPR is a program that produces orthophotos. The method used is to give colors to point-cloud (photo obtained by a digital camera, cloud obtained by a laser scanner) and to project the point-cloud to a requested plane. With this method, the projection pla
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connect3d
Reconstruct Water-Tight Triangulation from Point Cloud
This software reconstructs water-tight triangulations from point clouds, interpolating the points. It approximates the triangle mesh which minimizes the sum of all triangles' longest edge. As a result, it can interpolate much more sparse sampling as state-of-the-art algorithms. Run-time is in practice linear to that of the Delaunay triangulation of the points. The software is designed as a command-line tool. It can also be used as a library. A plug-in for the Meshlab geometry software is available as well. -
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This work presents an open-source tool for viewing the depth map information captured by Microsofts Kinect sensor in a distributed way as a 3D point cloud inside the web browser.
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ofxKuZed-zedExample
Compiled example for ofxKuZed addon
This is openFrameworks's ofxKuZed addon with compiled example of its usage. This example shows depth map, masked color image and point cloud from StereoLabs ZED. Requirements: - Windows - ZED SDK 1.0 or later (https://cloud.mail.ru/public/8Luw/yPxdYh91A) - CUDA 7.5 (and NVidia card) https://cloud.mail.ru/public/79z4/164HTQkeq - Visual Studio 2015 Community C++ (or its redistributables https://cloud.mail.ru/public/AgYD/r9JLgBc9y) See details on installation and example usage at GitHub: https://github.com/kuflex/ofxKuZed -
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pyntcloud
pyntcloud is a Python library for working with 3D point clouds
This page will introduce the general concept of point clouds and illustrate the capabilities of pyntcloud as a point cloud processing tool. Point clouds are one of the most relevant entities for representing three dimensional data these days, along with polygonal meshes (which are just a special case of point clouds with connectivity graph attached). In its simplest form, a point cloud is a set of points in a cartesian coordinate system. Accurate 3D point clouds can nowadays be (easily and cheaply) acquired from different sources. pyntcloud enables simple and interactive exploration of point cloud data, regardless of which sensor was used to generate it or what the use case is. Although it was built for being used on Jupyter Notebooks, the library is suitable for other kinds of uses. pyntcloud is composed of several modules (as independent as possible) that englobe common point cloud processing operations.
