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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 191–200 of 876 papers

TitleStatusHype
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
Depth Prompting for Sensor-Agnostic Depth EstimationCode0
FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth EstimationCode0
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Depth Priors in Removal Neural Radiance Fields—0
Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting—0
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images—0
The Third Monocular Depth Estimation Challenge—0
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