単眼深度補完を事前情報としたエピポーラ誘導オプティカルフローによる卓上透明シーンの再構成
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior
単眼のセグメンテーションと深度補完で透明物体の形状事前分布を得た後、エピポーラ誘導オプティカルフローで複数視点の情報を統合し、透明物体を3D再構成する手法を提案した。
著者: Xiaotong Chen, Zheming Zhou, Zhuo Deng, Omid Ghasemalizadeh, Min Sun, Cheng-Hao Kuo, Arnie Sen
分類: cs.RO, cs.CV
原文アブストラクト
Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view. We introduce a two-stage pipeline for reconstructing transparent objects tailored for mobile platforms. In the first stage, off-the-shelf monocular object segmentation and depth completion networks are leveraged to predict the depth of transparent objects, furnishing single-view shape prior. Subsequently, we propose Epipolar-guided Optical Flow (EOF) to fuse several single-view shape priors from the first stage to a cross-view consistent 3D reconstruction given camera poses estimated from opaque part of the scene. Our key innovation lies in EOF which employs boundary-sensitive sampling and epipolar-line constraints into optical flow to accurately establish 2D correspondences across multiple views on transparent objects. Quantitative evaluations demonstrate that our pipeline significantly outperforms baseline methods in 3D reconstruction quality, paving the way for more adept robotic perception and interaction with transparent objects.
