Quasi-dense 3D reconstruction using tensor-based multiview stereo.
ABSTRACT We propose tensor-based multiview stereo (TMVS) for quasi-dense D reconstruction from uncalibrated images. Our work is inspired by the patch-based multiview stereo (PMVS), a state-of-the-art technique in multiview stereo reconstruction. The effectiveness of PMVS is attributed to the use of 3D patches in the match-propagate-filter MVS pipeline. Our key observation is: PMVS has not fully utilized the valuable 3D geometric cue available in 3D patches which are oriented points. This paper combines the complementary advantages of photoconsistency, visibility and geometric consistency enforcement in MVS via the use of 3D tensors, where our closed-form solution to tensor voting provides a unified approach to implement the match-propagate-filter pipeline. Using PMVS as the implementation backbone where TMVS is built, we provide qualitative and quantitative evaluation to demonstrate how TMVS significantly improve the MVS pipeline.
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Article: The EM Algorithm and Extensions
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ABSTRACT: We address the problem of simultaneous two-view epipolar geometry estimation and motion segmentation from nonstatic scenes. Given a set of noisy image pairs containing matches of n objects, we propose an unconventional, efficient, and robust method, 4D tensor voting, for estimating the unknown n epipolar geometries, and segmenting the static and motion matching pairs into n independent motions. By considering the 4D isotropic and orthogonal joint image space, only two tensor voting passes are needed, and a very high noise to signal ratio (up to five) can be tolerated. Epipolar geometries corresponding to multiple, rigid motions are extracted in succession. Only two uncalibrated frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) other than the pin-hole camera model is made. Our novel approach consists of propagating a local geometric smoothness constraint in the 4D joint image space, followed by global consistency enforcement for extracting the fundamental matrices corresponding to independent motions. We have performed extensive experiments to compare our method with some representative algorithms to show that better performance on nonstatic scenes are achieved. Results on challenging data sets are presented.IEEE Transactions on Pattern Analysis and Machine Intelligence 10/2004; 26(9):1167-84. · 4.80 Impact Factor
Conference Proceeding: Multi-View Stereo for Community Photo Collections[show abstract] [hide abstract]
ABSTRACT: We present a multi-view stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a per-view and per-pixel level. We show that such adaptive view selection enables robust performance even with dramatic appearance variability. The stereo matching technique takes as input sparse 3D points reconstructed from structure-from-motion methods and iteratively grows surfaces from these points. Optimizing for surface normals within a photoconsistency measure significantly improves the matching results. While the focus of our approach is to estimate high-quality depth maps, we also show examples of merging the resulting depth maps into compelling scene reconstructions. We demonstrate our algorithm on standard multi-view stereo datasets and on casually acquired photo collections of famous scenes gathered from the Internet.Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on; 11/2007