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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 601–610 of 876 papers

TitleStatusHype
BlindSpotNet: Seeing Where We Cannot See—0
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth EstimationCode0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
Recovering Detail in 3D Shapes Using Disparity Maps—0
Monocular Depth Decomposition of Semi-Transparent Volume RenderingsCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation TechniquesCode0
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks—0
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
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