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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 411–420 of 876 papers

TitleStatusHype
Depth Estimation with Simplified Transformer—0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
Depth Estimation from Single Image using Sparse Representations—0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
Learning Good Features to Transfer Across Tasks and Domains—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Depth-Relative Self Attention for Monocular Depth Estimation—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
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