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Relative order constraint for monocular depth estimation
Monocular depth estimation, which is playing an increasingly important role in 3D scene understanding, has been attracting increasing attention in...
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DCL-depth: monocular depth estimation network based on iam and depth consistency loss
The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised...
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Resolution-sensitive self-supervised monocular absolute depth estimation
Depth estimation is an essential component of computer vision applications for environment perception, 3D reconstruction and scene understanding....
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Apply Fuzzy Mask to Improve Monocular Depth Estimation
A fuzzy mask applied to pixel-wise dissimilarity weighting is proposed to improve the monocular depth estimation in this study. The parameters in the...
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Monocular depth estimation via cross-spectral stereo information fusion
Although amount of works are focused on monocular depth estimation, these works mainly study on the RGB spectrum, which has a poor performance on the...
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Multi-feature fusion enhanced monocular depth estimation with boundary awareness
Self-supervised monocular depth estimation has opened up exciting possibilities for practical applications, including scene understanding, object...
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AMENet is a monocular depth estimation network designed for automatic stereoscopic display
Monocular depth estimation has a wide range of applications in the field of autostereoscopic displays, while accuracy and robustness in complex...
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Influence of Neural Network Receptive Field on Monocular Depth and Ego-Motion Estimation
AbstractWe present an analysis of a self-supervised learning approach for monocular depth and ego-motion estimation. This is an important problem for...
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Sparse depth densification for monocular depth estimation
Now the dense depth prediction by single image and a few sparse depth measurements has attracted more and more attention because it provides a...
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DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation
This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the...
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Chfnet: a coarse-to-fine hierarchical refinement model for monocular depth estimation
In recent years, many researchers have exploited multiple depth estimation architectures to produce high-quality depth maps from a single image. For...
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Self-Supervised Monocular Depth Estimation by Digging into Uncertainty Quantification
Based on well-designed network architectures and objective functions, self-supervised monocular depth estimation has made great progress. However,...
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EMTNet: efficient mobile transformer network for real-time monocular depth estimation
Estimating depth from a single image presents a formidable challenge due to the inherently ill-posed and ambiguous nature of deriving depth...
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Dual-attention-based semantic-aware self-supervised monocular depth estimation
Based on the assumption of photometric consistency, self-supervised monocular depth estimation has been widely studied due to the advantage of...
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Towards Robust Monocular Depth Estimation: A New Baseline and Benchmark
Before deploying a monocular depth estimation (MDE) model in real-world applications such as autonomous driving, it is critical to understand its...
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On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation
Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric...
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DAttNet: monocular depth estimation network based on attention mechanisms
As autonomous vehicles get closer to our daily lives, the need for architectures that function as redundant pipelines is becoming increasingly...
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TAMDepth: self-supervised monocular depth estimation with transformer and adapter modulation
Self-supervised monocular depth estimation presents a promising result, which utilizes image sequences instead of challenging-to-source ground truth...
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OptiDepthNet: A Real-Time Unsupervised Monocular Depth Estimation Network
With the development of deep learning, the network architectures and algorithm accuracy applied to monocular depth estimation have been greatly...
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Confidence-aware self-supervised learning for dense monocular depth estimation in dynamic laparoscopic scene
This paper tackles the challenge of accurate depth estimation from monocular laparoscopic images in dynamic surgical environments. The lack of...
