Learning to Predict Lidar Intensities

Patrik Vacek, Otakar Jasek, Karel Zimmermann, Tomás Svoboda

Abstract: We propose a data-driven method for simulating lidar sensors. The method reads computer-generated data, and (i) extracts geometrically simulated lidar point clouds and (ii) predicts the strength of the lidar response – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lidar intensities</i> . Qualitative evaluation of the proposed pipeline demonstrates the ability to predict systematic failures such as no/low responses on polished parts of car bodyworks and windows, or strong responses on reflective surfaces such as traffic signs and license/registration plates. We also experimentally show that enhancing the training set by such simulated data improves the segmentation accuracy on the real dataset with limited access to real data. Implementation of the resulting lidar simulator for the GTA V game, as well as the accompanying large dataset, is made publicly available.