DeepMIF: Deep Monotonic Implicit Fields for Large-Scale LiDAR 3D Mapping

arXiv:2403.17550v1 Announce Type: cross
Abstract: Recently, significant progress has been achieved in sensing real large-scale outdoor 3D environments, particularly by using modern acquisition equipment such as LiDAR sensors. Unfortunately, they are fundamentally limited in their ability to produce dense, complete 3D scenes. To address this issue, recent learning-based methods integrate neural implicit representations and optimizable feature grids to approximate surfaces of 3D scenes. However, naively fitting samples along raw LiDAR rays leads to noisy 3D mapping results due to the …

3d mapping 3d scenes abstract acquisition arxiv cs.cv cs.lg cs.ro environments equipment fields issue lidar mapping modern progress scale sensing sensors type