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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 451–475 of 876 papers

TitleStatusHype
Depth Is All You Need for Monocular 3D Detection—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax—0
Depth Priors in Removal Neural Radiance Fields—0
Depth-Relative Self Attention for Monocular Depth Estimation—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Diffusion-based Light Field Synthesis—0
Disambiguating Monocular Depth Estimation with a Single Transient—0
Discrete-Continuous Depth Estimation from a Single Image—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap—0
D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video—0
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Double Refinement Network for Efficient Indoor Monocular Depth Estimation—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
EgoM2P: Egocentric Multimodal Multitask Pretraining—0
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors—0
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