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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 311–320 of 876 papers

TitleStatusHype
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
Out-of-Distribution Detection for Monocular Depth EstimationCode1
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models—0
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution NetworkCode1
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—0
EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer—0
Robust Self-Supervised Extrinsic Self-Calibration—0
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
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