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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 651–660 of 876 papers

TitleStatusHype
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Rethinking Monocular Depth Estimation with Adversarial Training—0
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering—0
Robust Monocular Depth Estimation under Challenging Conditions—0
Robust Self-Supervised Extrinsic Self-Calibration—0
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances—0
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