98 research outputs found
Planar Prior Assisted PatchMatch Multi-View Stereo
The completeness of 3D models is still a challenging problem in multi-view
stereo (MVS) due to the unreliable photometric consistency in low-textured
areas. Since low-textured areas usually exhibit strong planarity, planar models
are advantageous to the depth estimation of low-textured areas. On the other
hand, PatchMatch multi-view stereo is very efficient for its sampling and
propagation scheme. By taking advantage of planar models and PatchMatch
multi-view stereo, we propose a planar prior assisted PatchMatch multi-view
stereo framework in this paper. In detail, we utilize a probabilistic graphical
model to embed planar models into PatchMatch multi-view stereo and contribute a
novel multi-view aggregated matching cost. This novel cost takes both
photometric consistency and planar compatibility into consideration, making it
suited for the depth estimation of both non-planar and planar regions.
Experimental results demonstrate that our method can efficiently recover the
depth information of extremely low-textured areas, thus obtaining high complete
3D models and achieving state-of-the-art performance.Comment: Accepted by AAAI-202
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
Deep learning has shown to be effective for depth inference in multi-view
stereo (MVS). However, the scalability and accuracy still remain an open
problem in this domain. This can be attributed to the memory-consuming cost
volume representation and inappropriate depth inference. Inspired by the
group-wise correlation in stereo matching, we propose an average group-wise
correlation similarity measure to construct a lightweight cost volume. This can
not only reduce the memory consumption but also reduce the computational burden
in the cost volume filtering. Based on our effective cost volume
representation, we propose a cascade 3D U-Net module to regularize the cost
volume to further boost the performance. Unlike the previous methods that treat
multi-view depth inference as a depth regression problem or an inverse depth
classification problem, we recast multi-view depth inference as an inverse
depth regression task. This allows our network to achieve sub-pixel estimation
and be applicable to large-scale scenes. Through extensive experiments on DTU
dataset and Tanks and Temples dataset, we show that our proposed network with
Correlation cost volume and Inverse DEpth Regression (CIDER), achieves
state-of-the-art results, demonstrating its superior performance on scalability
and accuracy.Comment: Accepted by AAAI-202
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