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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 726–750 of 876 papers

TitleStatusHype
UASOL, a large-scale high-resolution outdoor stereo dataset—0
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation—0
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian—0
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning—0
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models—0
Unsupervised High-Resolution Depth Learning From Videos With Dual Networks—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics—0
Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion—0
Unsupervised monocular stereo matching—0
Unsupervised Simultaneous Learning for Camera Re-Localization and Depth Estimation from Video—0
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios—0
Vision-Language Embodiment for Monocular Depth Estimation—0
Vision Transformer based Random Walk for Group Re-Identification—0
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation—0
VisualEchoes: Spatial Image Representation Learning through Echolocation—0
V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations—0
WaveShot: A Compact Portable Unmanned Surface Vessel for Dynamic Water Surface Videography and Media Production—0
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—0
Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?Code0
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