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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 491–500 of 876 papers

TitleStatusHype
From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior—0
GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding—0
Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Camera-Independent Single Image Depth Estimation from Defocus BlurCode0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
PolyMaX: General Dense Prediction with Mask Transformer—0
Analysis of NaN Divergence in Training Monocular Depth Estimation Model—0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
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