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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 241–250 of 876 papers

TitleStatusHype
Learning optical flow from still imagesCode1
S2R-DepthNet: Learning a Generalizable Depth-specific Structural RepresentationCode1
Deep Two-View Structure-from-Motion RevisitedCode1
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-SupervisionCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth ResidualsCode1
R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth EstimatingCode1
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
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