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Monocular Depth Estimation

Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity. The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error.

Source: Defocus Deblurring Using Dual-Pixel Data

Papers

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Showing 726–750 of 876 papers

TitleStatusHype
P2D: a self-supervised method for depth estimation from polarimetry—0
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models—0
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy—0
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation—0
An Advert Creation System for 3D Product Placements—0
Increased-Range Unsupervised Monocular Depth Estimation—0
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues—0
AcED: Accurate and Edge-consistent Monocular Depth Estimation—0
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation—0
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Monocular Depth Estimators: Vulnerabilities and Attacks—0
VisualEchoes: Spatial Image Representation Learning through Echolocation—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Monocular Depth Estimation with Self-supervised Instance Adaptation—0
Adversarial Attacks on Monocular Depth Estimation—0
Monocular Depth Estimation Based On Deep Learning: An Overview—0
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
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