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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

Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation

DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image

Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model

Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces

One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images

Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous Driving

FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion

Radar-Guided Polynomial Fitting for Metric Depth Estimation

Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation

UniK3D: Universal Camera Monocular 3D Estimation

QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge

3D Densification for Multi-Map Monocular VSLAM in Endoscopy

Multi-view Reconstruction via SfM-guided Monocular Depth Estimation

Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion

UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler

Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator

OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images

Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining

Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion

CDGS: Confidence-Aware Depth Regularization for 3D Gaussian Splatting

SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition

Pre-training Auto-regressive Robotic Models with 4D Representations

Deep Neural Networks for Accurate Depth Estimation with Latent Space Features

CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images

Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding

PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks

Survey on Monocular Metric Depth Estimation

Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering

HSPFormer: Hierarchical Spatial Perception Transformer for Semantic Segmentation

MonSter: Marry Monodepth to Stereo Unleashes Power

StereoGen: High-quality Stereo Image Generation from a Single Image

A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation

RePoseD: Efficient Relative Pose Estimation With Known Depth Information