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PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility
[article]
2021
arXiv
pre-print
Preserved Fulltext
, normals, and visibility. ...
Recent learning-based multi-view stereo (MVS) methods show excellent performance with dense cameras and small depth ranges. ...
We propose PatchMatch-RL, an end-to-end trainable PatchMatch-based MVS approach that combines advantages of trainable costs and regularizations with pixelwise estimates of depth, normal, and visibility ...
arXiv:2108.08943v1
fatcat:47vla23zoveifdaywzdokaxsoe
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers depth and confidence. ...
To extract the depth maps, we combine traditional classification and regression in a novel manner. We verify the efficiency and effectiveness of our method on DTU, Tanks&Temples and ETH3D. ...
Patchmatch-rl: Deep mvs with pixelwise depth, nor-
depth-maps for multi-view stereo. ...
arXiv:2112.05126v1
fatcat:hwlfy6g5xvbzbkww5e2zihxthm
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In contrast, we propose constructing the cost volume by non-parametric depth distribution modeling to handle pixels with unimodal and multi-modal distributions. ...
Recent cost volume pyramid based deep neural networks have unlocked the potential of efficiently leveraging high-resolution images for depth inference from multi-view stereo. ...
Related works Deep Learning-based MVS methods adopt deep CNNs to infer the depth map for each view followed by a separate multiple-view fusion process for building 3D models. ...
arXiv:2205.03783v1
fatcat:siyzfen2zjdhflllwwcnf743pa