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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 721–730 of 876 papers

TitleStatusHype
Disambiguating Monocular Depth Estimation with a Single Transient—0
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets—0
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone—0
P2D: a self-supervised method for depth estimation from polarimetry—0
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models—0
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy—0
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation—0
An Advert Creation System for 3D Product Placements—0
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