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10/06/2022
Unsupervised confidence for LiDAR depth maps and applications
Depth perception is pivotal in many fields, such as robotics and autonom...
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04/04/2022
Monitoring social distancing with single image depth estimation
The recent pandemic emergency raised many challenges regarding the count...
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10/28/2021
Neural Disparity Refinement for Arbitrary Resolution Stereo
We introduce a novel architecture for neural disparity refinement aimed ...
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09/30/2021
Sensor-Guided Optical Flow
This paper proposes a framework to guide an optical flow network with ex...
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04/08/2021
Learning optical flow from still images
This paper deals with the scarcity of data for training optical flow net...
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01/02/2021
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Stereo matching is one of the most popular techniques to estimate dense ...
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08/17/2020
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation
In many fields, self-supervised learning solutions are rapidly evolving ...
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08/14/2020
Self-adapting confidence estimation for stereo
Estimating the confidence of disparity maps inferred by a stereo algorit...
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06/10/2020
Real-time single image depth perception in the wild with handheld devices
Depth perception is paramount to tackle real-world problems, ranging fro...
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05/13/2020
On the uncertainty of self-supervised monocular depth estimation
Self-supervised paradigms for monocular depth estimation are very appeal...
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03/31/2020
Distilled Semantics for Comprehensive Scene Understanding from Videos
Whole understanding of the surroundings is paramount to autonomous syste...
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11/22/2019
Learning End-To-End Scene Flow by Distilling Single Tasks Knowledge
Scene flow is a challenging task aimed at jointly estimating the 3D stru...
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04/08/2019
Learning monocular depth estimation infusing traditional stereo knowledge
Depth estimation from a single image represents a fascinating, yet chall...
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06/29/2018