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Found 35 papers, 25 papers with code
The Third Monocular Depth Estimation Challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling autonomous exploration of unknown environments.
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization
This paper proposes a new framework for depth completion robust against domain-shifting issues.
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture.
Active Stereo Without Pattern Projector
This paper proposes a novel framework integrating the principles of active stereo in standard passive camera systems without a physical pattern projector.
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction.
Learning Depth Estimation for Transparent and Mirror Surfaces
Inferring the depth of transparent or mirror (ToM) surfaces represents a hard challenge for either sensors, algorithms, or deep networks.
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
NeRF-Supervised Deep Stereo
We introduce a novel framework for training deep stereo networks effortlessly and without any ground-truth.
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features.
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization.
The Monocular Depth Estimation Challenge
This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset.
Cross-Spectral Neural Radiance Fields
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation.
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training.
Ranked #1 on
Monocular Depth Estimation
on KITTI
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation
We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences.
Open Challenges in Deep Stereo: the Booster Dataset
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities.
Neural Disparity Refinement for Arbitrary Resolution Stereo
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones.
SMD-Nets: Stereo Mixture Density Networks
Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging.
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 depth maps by finding the disparity between matching pixels on two, synchronized and rectified images.
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation
In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with supervised approaches.
Self-adapting confidence estimation for stereo
Estimating the confidence of disparity maps inferred by a stereo algorithm has become a very relevant task in the years, due to the increasing number of applications leveraging such cue.
Continual Adaptation for Deep Stereo
Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression.
Real-time single image depth perception in the wild with handheld devices
Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications.
On the uncertainty of self-supervised monocular depth estimation
Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all.
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research.
Distilled Semantics for Comprehensive Scene Understanding from Videos
Whole understanding of the surroundings is paramount to autonomous systems.
Learning End-To-End Scene Flow by Distilling Single Tasks Knowledge
Scene flow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment.
Guided Stereo Matching
Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch.
Learning monocular depth estimation infusing traditional stereo knowledge
Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications.
Ranked #44 on
Monocular Depth Estimation
on KITTI Eigen split
Real-time self-adaptive deep stereo
Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs.
Geometry meets semantics for semi-supervised monocular depth estimation
For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera.
Beyond local reasoning for stereo confidence estimation with deep learning
Confidence measures for stereo gained popularity in recent years due to their improved capability to detect outliers and the increasing number of applications exploiting these cues.
Learning monocular depth estimation with unsupervised trinocular assumptions
Obtaining accurate depth measurements out of a single image represents a fascinating solution to 3D sensing.
Ranked #60 on
Monocular Depth Estimation
on KITTI Eigen split
Towards real-time unsupervised monocular depth estimation on CPU
To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.
Quantitative Evaluation of Confidence Measures in a Machine Learning World
However, since then major breakthroughs happened in this field: the availability of much larger and challenging datasets, novel and more effective stereo algorithms including ones based on deep-learning and confidence measures leveraging on machine learning techniques.
