LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds

LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds This dataset contains data related to our LidarScout paper at High-Performance Graphics 2025. This paper proposes a method for instantly exploring huge, compressed point clouds (LAZ) without any pre-processing and no extra storage. Contained data is: LidarScout executable for Windows (requires CUDA) A screen-capture video as MP4 Source code for the viewer (Github Snapshot from 2025-09-25, github.com/cg-tuwien/lidarscout , mostly C++ and CUDA) Source code for the training (Github Snapshot from 2025-09-25, github.com/cg-tuwien/lidarscout_training , mostly Python) Trained models for all variants as Pytorch checkpoints (.ckpt) and Torchscript (.pt) Training and testing datasets based on AHN5, Bund_BoraPk, CA13, ID15_Bunds, NZ23_Gisborne, BR17_SaoPaulo, and SwissSurface3D as CSV, PLY, and BIN files.

Testing results for all model variants and all test datasets as PLY and NPY files More detailed information on the folder and file structure can be found in the README of the training repository. More general information: cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/ There are some scripts provided in the code archive for very interested users; they may require a little bit of tinkering to get them to run.