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20162022
most citedDeformable spatial propagation network for depth completion

10 citations · 24 across the 6 of their papers we have counts for

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Showing cs.CVShow all

12 papers · 1 filter

cs.CV2022

DeepMLE: A Robust Deep Maximum Likelihood Estimator for Two-view Structure from Motion

Yuxi Xiao, Li Li, Xiaodi Li +1

Two-view structure from motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM (vSLAM). Many existing end-to-end learning-based methods usually formulate it as a brut…

cs.CV20222 cited

PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction

Haonan Dong, Jian Yao

Learning-based multi-view stereo (MVS) has gained fine reconstructions on popular datasets. However, supervised learning methods require ground truth for training, which is hard to…

cs.CV20216 cited

DDR-Net: Learning Multi-Stage Multi-View Stereo With Dynamic Depth Range

Puyuan Yi, Shengkun Tang, Jian Yao

To obtain high-resolution depth maps, some previous learning-based multi-view stereo methods build a cost volume pyramid in a coarse-to-fine manner. These approaches leverage fixed…

cs.CV202010 cited

Deformable spatial propagation network for depth completion

Zheyuan Xu, Hongche Yin, Jian Yao

Depth completion has attracted extensive attention recently due to the development of autonomous driving, which aims to recover dense depth map from sparse depth measurements. Conv…

cs.CV20201 cited

Vanishing Point Guided Natural Image Stitching

Kai Chen, Jian Yao, Jingmin Tu +3

Recently, works on improving the naturalness of stitching images gain more and more extensive attention. Previous methods suffer the failures of severe projective distortion and un…

cs.CV2020

GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling

Yahui Liu, Marco De Nadai, Jian Yao +3

Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images. Recent studies have shown remarkable suc…