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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 211–220 of 876 papers

TitleStatusHype
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
Frequency-Aware Self-Supervised Monocular Depth EstimationCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth AdaptationCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation ModelCode1
Automated Distance Estimation for Wildlife Camera TrappingCode1
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