Open Source Mac LiDAR Software
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LiDAR Software for Mac
View 7 business solutionsBrowse free open source LiDAR software and projects for Mac below. Use the toggles on the left to filter open source LiDAR software by OS, license, language, programming language, and project status.
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SAGA - System for Automated Geoscientific Analyses - is a Geographic Information System (GIS) software with immense capabilities for geodata processing and analysis. SAGA is programmed in the object oriented C++ language and supports the implementation of new functions with a very effective Application Programming Interface (API). Functions are organised as modules in framework independent Module Libraries and can be accessed via SAGA’s Graphical User Interface (GUI) or various scripting environments (shell scripts, Python, R, ...). Please provide the following reference in your work if you are using SAGA: Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Boehner, J. (2015): System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev., 8, 1991-2007, https://doi.org/10.5194/gmd-8-1991-2015. For more information visit the project homepage and the wiki.
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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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LAStools
efficient tools for LiDAR processing
LAStools is a collection of efficient, multi-core, scriptable tools for processing LiDAR data. It supports various formats, including LAS, LAZ, Terrasolid BIN, and ESRI Shapefiles, providing a comprehensive suite for LiDAR data management and analysis. -
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Direct LiDAR Odometry
A lightweight and computationally-efficient frontend LiDAR odometry
DLO is a lightweight and computationally efficient frontend LiDAR odometry solution with consistent and accurate localization. It features several algorithmic innovations that increase speed, accuracy, and robustness of pose estimation in perceptually challenging environments and has been extensively tested on aerial and legged robots. This work was part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge, in which DLO was the primary state estimation component for our fleet of autonomous aerial vehicles. -
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lidarslam_ros2
ROS 2 package of 3D lidar slam using ndt/gicp registration
ROS 2 package of 3D lidar slam using ndt/gicp registration and pose-optimization. ros2 slam package of the frontend using OpenMP-boosted gicp/ndt scan matching and the backend using graph-based slam. -
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Direct LiDAR-Inertial Odometry
A new lightweight LiDAR-inertial odometry algorithm
DLIO is a new lightweight LiDAR-inertial odometry algorithm with a novel coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. It features several algorithmic improvements over its predecessor, DLO, and was presented at the IEEE International Conference on Robotics and Automation (ICRA) in London, UK in 2023. -
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MATLAB Deep Learning Model Hub
Discover pretrained models for deep learning in MATLAB
Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score. -
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whiteboxgui
An interactive GUI for WhiteboxTools in a Jupyter-based environment
The whiteboxgui Python package is a Jupyter frontend for WhiteboxTools, an advanced geospatial data analysis platform developed by Prof. John Lindsay (webpage; jblindsay) at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; etc. -
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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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GLTF/GLB Windows Shell Extension: This shell extension automatically adds thumbnail images to .glb files in Windows File Explorer and provides a right click menu option to generate a 3D preview image for .gltf files. Supports multi-file select and directory processing. Pre-built Windows x64 installer available at https://buymeacoffee.com/nathancrews/e/255640 LAS/LAZ Pointcloud Windows Shell Extension: This "quick viewer" shell extension provides a Windows File Explorer right click menu option to quickly generate 3D preview images for large and small .las and .laz files. (Disclaimer: Processing time depends on compute hardware) Typical processing time for 100mb .las is about 2 seconds. 25mb .laz file process in about the same times. Files are processed in parallel using available CPU cores. Pre-built Windows x64 installer available at https://buymeacoffee.com/nathancrews/e/255641
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MCC-LIDAR
Multiscale Curvature Classification for LIDAR Data
MCC-LIDAR is a C++ application for processing LiDAR data in forested environments. It classifies data points as ground or non-ground using the Multiscale Curvature Classification algorithm. -
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3Depict
atom probe software : visualisation and data analysis
This software is designed to help users visualize and analyze 3D point clouds with an associated real value, in a fast and flexible fashion. The primary use is in Atom Probe Tomography, which is an atomic imaging technique. However the program may also be useful in other areas, such as geospatial data, lidar, etc. -
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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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BALCAL: Balloon Absolute Lidar Calibration with A Lamp. Atmospheric calibration for astronomy using stable lamps mounted on a high-altitude balloon.
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries. To aggregate spatial information, we design spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose temporal self-attention to recurrently fuse the history BEV information. Our approach achieves the new state-of-the-art 56.9\% in terms of NDS metric on the nuScenes \texttt{test} set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baseline. -
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Segments.ai
Segments.ai Python SDK
Multi-sensor labeling platform for robotics and autonomous vehicles. The platform for fast and accurate multi-sensor data annotation. Label in-house or with an external workforce. Intuitive labeling interfaces for images, videos, and 3D point clouds (lidar and RGBD). Obtain segmentation labels, vector labels, and more. Our labeling interfaces are set up to label fast and precise. Powerful ML assistance lets you label faster and reduce costs. Integrate data labeling into your existing ML pipelines and workflows using our simple yet powerful Python SDK. Onboard your own workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together. Now, Segments.ai is providing a data labeling backbone to help robotics and AV companies build better datasets. -
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mabelidl
IDL programs used to work with Mabel lidar data.
These are IDL programs that we use to browse and process Mabel Lidar data. Complete instructions for installing and using the software can be found on the Wiki pages. A current version (very current!) of the Coyote Library is required. -
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This project is a prototype of 3D modelisation using a sick LMS 100 LIDAR. The driver was created in JAVA. This is developped under a GNU GPL Licence. ESIEA Paris ATIS Lab team: NICOL Anne-Gaëlle / BOUJAADA Chafik /GERMAIN Josselin/ MALLIN Jeremy
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