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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 181–190 of 876 papers

TitleStatusHype
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World AttacksCode1
Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single ImageCode3
MambaDepth: Enhancing Long-range Dependency for Self-Supervised Fine-Structured Monocular Depth Estimation—0
Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry—0
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual OdometryCode4
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian—0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
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