LiStereo: LIDARとステレオ画像から高密度深度マップを生成
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery
LIDARの正確だが疎な深度とステレオ画像の密だが誤差のある深度を自己教師あり学習で融合し、低解像度入力でも高品質な高密度深度マップを生成する手法を提案。
著者: Junming Zhang, Manikandasriram Srinivasan Ramanagopal, Ram Vasudevan, Matthew Johnson-Roberson
分類: cs.RO, cs.CV
原文アブストラクト
An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matching algorithms are used to acquire such depth information. However, a high-resolution LIDAR is expensive and produces sparse depth map at large range; stereo matching algorithms are able to generate denser depth maps but are typically less accurate than LIDAR at long range. This paper combines these approaches together to generate high-quality dense depth maps. Unlike previous approaches that are trained using ground-truth labels, the proposed model adopts a self-supervised training process. Experiments show that the proposed method is able to generate high-quality dense depth maps and performs robustly even with low-resolution inputs. This shows the potential to reduce the cost by using LIDARs with lower resolution in concert with stereo systems while maintaining high resolution.
