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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ABSTRACT: This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.IEEE Transactions on Pattern Analysis and Machine Intelligence 11/2011; 33(11):2215-28. DOI:10.1109/TPAMI.2011.23 · 5.69 Impact Factor
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ABSTRACT: Patch cloud based multi-view stereo methods have proven to be an accurate and scalable approach for scene reconstruction. Their applicability, however, is limited due to the semi-dense nature of their reconstruction. We propose a method to generate a dense depth map from a patch cloud by assuming a planar surface model for non-reconstructed areas. We use local evidence to estimate the best fitting plane around missing areas. We then apply a graph cut optimization to select the best plane for each pixel. We demonstrate our approach with a challenging scene containing planar and non-planar surfaces.3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2011; 06/2011
- New Developments in the Visualization and Processing of Tensor Fields :, 01/2012: pages 29-50;