LiDARを用いた効率的な暗黙的ニューラル再構成
Efficient Implicit Neural Reconstruction Using LiDAR
スパースなLiDAR点群と粗いオドメトリから、2Dレンダリングを介さず3D空間で直接監督する損失関数により、数分で高精細な暗黙的占有場を再構成する手法を提案。
著者: Dongyu Yan, Xiaoyang Lyu, Jieqi Shi, Yi Lin
分類: cs.RO, cs.CV
原文アブストラクト
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage. Previous approaches have demonstrated impressive results using color or depth images but still have difficulty handling poor light conditions and large-scale scenes. Methods taking global point cloud as input require accurate registration and ground truth coordinate labels, which limits their application scenarios. In this paper, we propose a new method that uses sparse LiDAR point clouds and rough odometry to reconstruct fine-grained implicit occupancy field efficiently within a few minutes. We introduce a new loss function that supervises directly in 3D space without 2D rendering, avoiding information loss. We also manage to refine poses of input frames in an end-to-end manner, creating consistent geometry without global point cloud registration. As far as we know, our method is the first to reconstruct implicit scene representation from LiDAR-only input. Experiments on synthetic and real-world datasets, including indoor and outdoor scenes, prove that our method is effective, efficient, and accurate, obtaining comparable results with existing methods using dense input.
