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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 711–720 of 876 papers

TitleStatusHype
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection—0
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Calibrating Self-supervised Monocular Depth Estimation—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss—0
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