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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 541–550 of 876 papers

TitleStatusHype
Improving Monocular Visual Odometry Using Learned Depth—0
Improving Online Performance Prediction for Semantic Segmentation—0
Improving the Reliability for Confidence Estimation—0
Increased-Range Unsupervised Monocular Depth Estimation—0
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes—0
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces—0
Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting—0
Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence—0
Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation—0
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation—0
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