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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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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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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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Monocular human depth estimation with 3D motion flow and surface normals
We propose a novel monocular human depth estimation method using video sequences as training data. We jointly train the depth and 3D motion flow...
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Car depth estimation within a monocular image using a light CNN
Mobile intelligent systems that need to perceive the environment and move in it must measure its depth. Therefore, this issue is pervasive in...
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Towards a Unified Network for Robust Monocular Depth Estimation: Network Architecture, Training Strategy and Dataset
Robust monocular depth estimation (MDE) aims at learning a unified model that works across diverse real-world scenes, which is an important and...
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EDFIDepth: enriched multi-path vision transformer feature interaction networks for monocular depth estimation
Monocular depth estimation (MDE) aims to predict pixel-level dense depth maps from a single RGB image. Some recent approaches mainly rely on...
