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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 341–350 of 876 papers

TitleStatusHype
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehiclesCode1
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality DecoderCode0
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth AdaptationCode1
Hierarchical Neural Memory Network for Low Latency Event ProcessingCode0
Polarimetric Imaging for Perception—0
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance FieldsCode1
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
A Multi-modal Approach to Single-modal Visual Place Classification—0
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume—0
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