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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 521–530 of 876 papers

TitleStatusHype
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth EstimationCode2
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware FusionCode1
How Much Depth Information can Radar Contribute to a Depth Estimation Model?—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
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
Automated Distance Estimation for Wildlife Camera TrappingCode1
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera IntrinsicsCode1
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
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