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09/21/2023
Active Stereo Without Pattern Projector
This paper proposes a novel framework integrating the principles of acti...
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09/05/2023
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
Neural implicit representations have recently demonstrated compelling re...
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07/27/2023
Learning Depth Estimation for Transparent and Mirror Surfaces
Inferring the depth of transparent or mirror (ToM) surfaces represents a...
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04/14/2023
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular...
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03/30/2023
NeRF-Supervised Deep Stereo
We introduce a novel framework for training deep stereo networks effortl...
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03/16/2023
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
We propose a novel multi-stage depth super-resolution network, which pro...
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01/19/2023
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Estimating depth from images nowadays yields outstanding results, both i...
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11/22/2022
The Monocular Depth Estimation Challenge
This paper summarizes the results of the first Monocular Depth Estimatio...
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09/01/2022
Cross-Spectral Neural Radiance Fields
We propose X-NeRF, a novel method to learn a Cross-Spectral scene repres...
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08/06/2022
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Self-supervised monocular depth estimation is an attractive solution tha...
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06/14/2022
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation
We address the problem of registering synchronized color (RGB) and multi...
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06/09/2022
Open Challenges in Deep Stereo: the Booster Dataset
We present a novel high-resolution and challenging stereo dataset framin...
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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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04/08/2021
SMD-Nets: Stereo Mixture Density Networks
Despite stereo matching accuracy has greatly improved by deep learning i...
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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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07/10/2020
Continual Adaptation for Deep Stereo
Depth estimation from stereo images is carried out with unmatched result...
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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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04/18/2020
On the Synergies between Machine Learning and Stereo: a Survey
Stereo matching is one of the longest-standing problems in computer visi...
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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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05/24/2019
Guided Stereo Matching
Stereo is a prominent technique to infer dense depth maps from images, a...
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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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10/12/2018
Real-time self-adaptive deep stereo
Deep convolutional neural networks trained end-to-end are the undisputed...
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10/09/2018
Geometry meets semantics for semi-supervised monocular depth estimation
Depth estimation from a single image represents a very exciting challeng...
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08/05/2018
Learning monocular depth estimation with unsupervised trinocular assumptions
Obtaining accurate depth measurements out of a single image represents a...
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06/29/2018