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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 641–650 of 876 papers

TitleStatusHype
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis—0
Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images—0
Occlusion-Ordered Semantic Instance Segmentation—0
OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
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