2 citations · 4 across the 4 of their papers we have counts for
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cs.CV2022★ 2 cited
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
Zhiqi Zhang, Nitin Bansal, Changjiang Cai +4
Synthetic datasets are often used to pretrain end-to-end optical flow networks, due to the lack of a large amount of labeled, real-scene data. But major drops in accuracy occur whe…
cs.CV2022★ 1 cited
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
Qingan Yan, Pan Ji, Nitin Bansal +3
In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner. A known issue of self-supervised depth estimation is that it…
cs.CV2022
GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping
Pan Ji, Qingan Yan, Yuxin Ma +1
We present a robust and accurate depth refinement system, named GeoRefine, for geometrically-consistent dense mapping from monocular sequences. GeoRefine consists of three modules:…