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szenergy/awesome-lidar
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
A well-maintained 'awesome list' for the LIDAR/point-cloud ecosystem: hardware vendors, public datasets, core algorithms, and frameworks all in one place. Primarily useful for robotics and autonomous driving researchers getting oriented in this space, or practitioners who need a quick reference to find a specific dataset or library without already knowing its name.
The algorithm taxonomy is genuinely useful — ICP variants, SLAM systems, ground segmentation, and semantic segmentation are broken out separately rather than dumped in one list. Badge conventions (ROS 2 compatibility, paper links, YouTube demos) are consistently applied, which saves meaningful lookup time when evaluating whether something works in your stack. Dataset coverage is solid: KITTI, nuScenes, Waymo, Argoverse, and several less-obvious ones like CADC (adverse weather) and Boreas (seasonal variation) are all present. Reasonably current — entries like KISS-ICP (2022) and GSeg3D (2025) show active curation.
It's a list, not a guide — there's no opinion on when you'd pick PCL over Open3D, or KISS-ICP over full LOAM, which is exactly what someone new to the space needs. Some manufacturer entries are clearly stale: Velodyne/Ouster merged in 2023 and the entry half-reflects that, and Ibeo went bankrupt in 2022. The algorithms section has a strong academic bias toward Bonn/PRBonn research output, which is excellent work, but newer industry-relevant methods (e.g., 3D Gaussian splatting for mapping, learned descriptors like LoGG3D-Net) are absent. The repo itself is just a markdown file and a CSV with no clear schema, so it's not machine-queryable or embeddable in tooling.