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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 426–450 of 876 papers

TitleStatusHype
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
METER: a mobile vision transformer architecture for monocular depth estimationCode0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
Dual CNN Models for Unsupervised Monocular Depth EstimationCode0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
Self-Supervised Monocular Depth HintsCode0
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
Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models—0
Improving Depth Estimation using Location Information—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
3D Densification for Multi-Map Monocular VSLAM in Endoscopy—0
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces—0
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