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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 351–375 of 876 papers

TitleStatusHype
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images—0
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments—0
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary TasksCode0
Survey on Monocular Metric Depth Estimation—0
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering—0
StereoGen: High-quality Stereo Image Generation from a Single Image—0
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation—0
Vision-Language Embodiment for Monocular Depth Estimation—0
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning—0
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
Learning Monocular Depth from Events via Egomotion Compensation—0
Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for Rescaling Disparity for Zero-Shot Metric Depth Estimation—0
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion—0
V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations—0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation—0
GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion—0
LAA-Net: A Physical-prior-knowledge Based Network for Robust Nighttime Depth Estimation—0
Align3R: Aligned Monocular Depth Estimation for Dynamic Videos—0
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation—0
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation—0
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