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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 681–690 of 876 papers

TitleStatusHype
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation—0
Semi-Supervised Adversarial Monocular Depth Estimation—0
Semi-Supervised Deep Learning for Monocular Depth Map Prediction—0
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation—0
SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction—0
SGTBN: Generating Dense Depth Maps from Single-Line LiDAR—0
SLAM Endoscopy enhanced by adversarial depth prediction—0
SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots—0
Soft Labels for Ordinal Regression—0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
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