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Monocular Depth Estimation

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보존된 평가 기록: 283

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

논문

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Monocular Depth Estimation

논문

Relative Pose Estimation through Affine Corrections of Monocular Depth Priors

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning

Learning Monocular Depth from Events via Egomotion Compensation

Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement

Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion

Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for Rescaling Disparity for Zero-Shot Metric Depth Estimation

V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations

Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation

GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion

LAA-Net: A Physical-prior-knowledge Based Network for Robust Nighttime Depth Estimation

Align3R: Aligned Monocular Depth Estimation for Dynamic Videos

STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation

MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications

Spatially Visual Perception for End-to-End Robotic Learning

PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation

OceanLens: An Adaptive Backscatter and Edge Correction using Deep Learning Model for Enhanced Underwater Imaging

Scalable Autoregressive Monocular Depth Estimation

MGNiceNet: Unified Monocular Geometric Scene Understanding

MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth Estimation

Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching

OSMLoc: Single Image-Based Visual Localization in OpenStreetMap with Fused Geometric and Semantic Guidance

Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation

D$^3$epth: Self-Supervised Depth Estimation with Dynamic Mask in Dynamic Scenes

Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training

PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes

ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images

Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving

PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting

Depth Attention for Robust RGB Tracking

Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images

DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain

Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimation

Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation

Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models