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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 101–110 of 876 papers

TitleStatusHype
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving—0
ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D ImagesCode2
PF3plat: Pose-Free Feed-Forward 3D Gaussian SplattingCode3
Depth Attention for Robust RGB TrackingCode1
Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared imagesCode1
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
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