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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 631–640 of 876 papers

TitleStatusHype
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
How Much Depth Information can Radar Contribute to a Depth Estimation Model?—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
Maximum Likelihood Uncertainty Estimation: Robustness to OutliersCode0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding—0
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