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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 301–325 of 876 papers

TitleStatusHype
Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric FusionCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the DarkCode1
One Shot 3D PhotographyCode1
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimationCode1
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors—0
EgoM2P: Egocentric Multimodal Multitask Pretraining—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
Composite Learning for Robust and Effective Dense Predictions—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
Improving Depth Estimation using Location Information—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
Improving Monocular Visual Odometry Using Learned Depth—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
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