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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 441–450 of 876 papers

TitleStatusHype
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—0
Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models—0
Improving Depth Estimation using Location Information—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
3D Densification for Multi-Map Monocular VSLAM in Endoscopy—0
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces—0
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