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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 511–520 of 876 papers

TitleStatusHype
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Composite Learning for Robust and Effective Dense Predictions—0
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR—0
CroMo: Cross-Modal Learning for Monocular Depth Estimation—0
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy—0
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