Reconstruction et complétion 3D de la scène pour la conduite autonome
Luis Guillermo Roldão Jimenez · HAL (Le Centre pour la Communication Scientifique Directe) · 2021
In this thesis, we address the challenges of 3D scene reconstruction and completion from sparse and heterogeneous density point clouds. Therefore proposing different techniques to create a 3D model of the surroundings.In the first part, we study the use of 3-dimensional occupancy grids for multi-frame reconstruction, useful for localization and HD-Maps applications. This is done by exploiting ray-path information to resolve ambiguities in partially occupied cells. Our sensor model reduces discretization inaccuracies and enables occupancy updates in dynamic scenarios.We also focus on single-frame environment perception by the introduction of a 3D implicit surface reconstruction algorithm capable to deal with heterogeneous density data by employing an adaptive neighborhood strategy. Our method completes small regions of missing data and outputs a continuous representation useful for physical modeling or terrain traversability assessment.We dive into deep learning applications for the novel task of semantic scene completion, which completes and semantically annotates entire 3D input scans. Given the little consensus found in the literature, we present an in-depth survey of existing methods and introduce our lightweight multiscale semantic completion network for outdoor scenarios. Our method employs a new hybrid pipeline based on a 2D CNN backbone branch to reduce computation overhead and 3D segmentation heads to predict the complete semantic scene at different scales, being significantly lighter and faster than existing approaches.