2019/05/17 by Ahmad El Sallab, Sallab, Ahmad El, Ibrahim Sobh +5 · 2 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Advanced Vision and Imaging #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Robotics (cs.RO) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1905.07290
openalex publication_date 2019/05/17 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28
In the autonomous driving domain, data collection and annotation from real vehicles are expensive and sometimes unsafe. Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms. Instead, sensors models can be learned from real data. The main challenge is the absence of paired data set, which makes traditional supervised learning techniques not suitable. In this work, we formulate the problem as image translation from unpaired data and employ CycleGANs to solve the sensor modeling problem for LiDAR, to produce realistic LiDAR from simulated LiDAR (sim2real). Further, we generate high-resolution, realistic LiDAR from lower resolution one (real2real). The LiDAR 3D point cloud is processed in Bird-eye View and Polar 2D representations. The experimental results show a high potential of the proposed approach.