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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 691–700 of 876 papers

TitleStatusHype
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Deep Learning--Based Scene Simplification for Bionic VisionCode0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Monocular Depth Estimation for Soft Visuotactile Sensors—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
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