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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...

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.

...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.

...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.

...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.

...(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

A new algorithm for extracting roof tops was developed. Using the assumption that roof tops are planar in construction, a new approach was developed using volume of point clouds to determine whether a cluster contains planar points. This approach yields very promising results and with attention applied to its weaknesses, should provide another algorithm which can rival currently available roof top detection methods.

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.

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.

BALCAL: Balloon Absolute Lidar Calibration with A Lamp. Atmospheric calibration for astronomy using stable lamps mounted on a high-altitude balloon.

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