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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 531–540 of 876 papers

TitleStatusHype
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality DecoderCode0
Hierarchical Neural Memory Network for Low Latency Event ProcessingCode0
Polarimetric Imaging for Perception—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction—0
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume—0
A Multi-modal Approach to Single-modal Visual Place Classification—0
AutoColor: Learned Light Power Control for Multi-Color HologramsCode0
High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation—0
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