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ResDepth: Learned Residual Stereo Reconstruction
[article]
2021
arXiv
pre-print
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The strategy to only learn the residual greatly simplifies the learning problem. ...
We propose an embarrassingly simple but very effective scheme for high-quality dense stereo reconstruction: (i) generate an approximate reconstruction with your favourite stereo matcher; (ii) rewarp the ...
ResDepth-stereo further improves the reconstruction. ...
arXiv:2001.08026v3
fatcat:w5sibck3ive7bnefqcp7iicwze
ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images
[article]
2021
arXiv
pre-print
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Modern optical satellite sensors enable high-resolution stereo reconstruction from space. ...
To that end, we introduce ResDepth, a convolutional neural network that learns such an expressive geometric prior from example data. ...
Here, we show that a residual learning strategy greatly simplifies the learning problem. ...
arXiv:2106.08107v2
fatcat:cvvsxqyn4ngcbbd4gd7qb7fhua
IMPROVING DISPARITY ESTIMATION BASED ON RESIDUAL COST VOLUME AND RECONSTRUCTION ERROR VOLUME
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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 2020; you can also visit the original URL.
The file type is application/pdf.
The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. ...
We evaluate our method on several challenging stereo datasets. ...
Most recently, ResDepth, a deep network (Stucker and Schindler, 2020) was proposed to improve the depth map for high-quality dense stereo reconstruction. ...
doi:10.5194/isprs-archives-xliii-b2-2020-135-2020
fatcat:xxjlnaj7orexhjfgggrcs23fkq
MetaMQAP: A meta-server for the quality assessment of protein models
2008
BMC Bioinformatics
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
As a reference, we calculated the value of correlation between the local deviations and trivial features that can be calculated for each residue directly from the models, i.e. solvent accessibility, depth ...
eight MQAPs: VERIFY3D, PROSA, BALA, ANOLEA, PROVE, TUNE, REFINER, PROQRES on 8251 models from the CASP-5 and CASP-6 experiments, by calculating the Spearman's rank correlation coefficients between per-residue ...
This procedure reconstructed a heavy-atom representation for all residues except the omitted terminal residues and optimized the bond lengths, angles and packing. ...
doi:10.1186/1471-2105-9-403
pmid:18823532
pmcid:PMC2573893
fatcat:olfvxgorjjaxvdzya37i6dgqgu
CVPRW 2020 TOC
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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The file type is application/pdf.
A Training Method for Image Compression Networks to Improve Perceptual Quality of Reconstructions 585 Jooyoung Lee (Broadcasting and Media Research Laboratory, Electronics and Telecommunications Research ...
: Learned Residual Stereo Reconstruction
707
Corinne Stucker (Photogrammetry and Remote Sensing, ETH Zurich,
Switzerland) and Konrad Schindler (Photogrammetry and Remote Sensing,
ETH Zurich, Switzerland ...
Attribute-Conditioned Synthesis
645
Zac Yu (University of Pittsburgh) and Adriana Kovashka (University of
Pittsburgh)
xvii
EarthVision: EarthVision: Large Scale Computer Vision for Remote
Sensing Imagery
ResDepth ...
doi:10.1109/cvprw50498.2020.00004
fatcat:6qao4eypyvg3xiiigih3kicniq
Improving disparity estimation based on residual cost volume and reconstruction error volume
[article]
2021
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.
The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. ...
We evaluate our method on several challenging stereo datasets. ...
Most recently, ResDepth, a deep network (Stucker and Schindler, 2020) was proposed to improve the depth map for high-quality dense stereo reconstruction. ...
doi:10.15488/10823
fatcat:6izsrmmdybhb7mxd45azkrw5tq