|
14 Oct 2022
SIMULATING LIDAR TO CREATE TRAINING DATA FOR MACHINE LEARNING ON 3D POINT CLOUDS
J. Hildebrand, S. Schulz, R. Richter, and J. Döllner
Related authors
A SERVICE-ORIENTED INDOOR POINT CLOUD PROCESSING PIPELINE
V. Stojanovic, M. Trapp, R. Richter, and J. Döllner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W17, 339–346, https://doi.org/10.5194/isprs-archives-XLII-2-W17-339-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W17-339-2019, 2019
Sensor Data Visualization for Indoor Point Clouds
Vladeta Stojanovic, Matthias Trapp, Benjamin Hagedorn, Jan Klimke, Rico Richter, and Jürgen Döllner
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 2, 13, https://doi.org/10.5194/ica-adv-2-13-2019,https://doi.org/10.5194/ica-adv-2-13-2019, 2019
COMBINED VISUAL EXPLORATION OF 2D GROUND RADAR AND 3D POINT CLOUD DATA FOR ROAD ENVIRONMENTS
J. Wolf, S. Discher, L. Masopust, S. Schulz, R. Richter, and J. Döllner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W10, 231–236, https://doi.org/10.5194/isprs-archives-XLII-4-W10-231-2018,https://doi.org/10.5194/isprs-archives-XLII-4-W10-231-2018, 2018