2023/06/12 by Alec Reed, Christoffer Heckman, Reed, Alec +1
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Surveying and Cultural Heritage #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2306.07160
openalex publication_date 2023/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we provide an early look at our model for generating terrain that is occluded in the initial lidar scan or out of range of the sensor. As a proof of concept, we show that a transformer based framework is able to be overfit to predict the geometries of unobserved roads around intersections or corners. We discuss our method for generating training data, as well as a unique loss function for training our terrain extension network. The framework is tested on data from the SemanticKitti [1] dataset. Unlabeled point clouds measured from an onboard lidar are used as input data to generate predicted road points that are out of range or occluded in the original point-cloud scan. Then the input pointcloud and predicted terrain are concatenated to the terrain-extended pointcloud. We show promising qualitative results from these methods, as well as discussion for potential quantitative metrics to evaluate the overall success of our framework. Finally, we discuss improvements that can be made to the framework for successful generalization to test sets.