移動カメラからの確率的密再構成
Probabilistic Dense Reconstruction from a Moving Camera
単眼カメラの移動中に得られる奥行き推定を時空間相関と不確かさ考慮で統合し、オンラインで密な3Dモデルを構築する手法を提案した論文。
著者: Yonggen Ling, Kaixuan Wang, Shaojie Shen
分類: cs.RO
原文アブストラクト
This paper presents a probabilistic approach for online dense reconstruction using a single monocular camera moving through the environment. Compared to spatial stereo, depth estimation from motion stereo is challenging due to insufficient parallaxes, visual scale changes, pose errors, etc. We utilize both the spatial and temporal correlations of consecutive depth estimates to increase the robustness and accuracy of monocular depth estimation. An online, recursive, probabilistic scheme to compute depth estimates, with corresponding covariances and inlier probability expectations, is proposed in this work. We integrate the obtained depth hypotheses into dense 3D models in an uncertainty-aware way. We show the effectiveness and efficiency of our proposed approach by comparing it with state-of-the-art methods in the TUM RGB-D SLAM and ICL-NUIM dataset. Online indoor and outdoor experiments are also presented for performance demonstration.
