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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 501–510 of 876 papers

TitleStatusHype
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs—0
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for Rescaling Disparity for Zero-Shot Metric Depth Estimation—0
Fractal Pyramid Networks—0
From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior—0
From Single Images to Motion Policies via Video-Generation Environment Representations—0
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume—0
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