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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 301–325 of 876 papers

TitleStatusHype
Digging Into Self-Supervised Monocular Depth EstimationCode1
Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style TransferCode1
Unsupervised Monocular Depth Estimation with Left-Right ConsistencyCode1
Deeper Depth Prediction with Fully Convolutional Residual NetworksCode1
Depth Map Prediction from a Single Image using a Multi-Scale Deep NetworkCode1
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios—0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way—0
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures—0
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation—0
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning—0
THIRDEYE: Cue-Aware Monocular Depth Estimation via Brain-Inspired Multi-Stage Fusion—0
Look to Locate: Vision-Based Multisensory Navigation with 3-D Digital Maps for GNSS-Challenged Environments—0
BulletGen: Improving 4D Reconstruction with Bullet-Time Generation—0
Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Grasping—0
EgoM2P: Egocentric Multimodal Multitask Pretraining—0
Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence—0
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
Toward Better SSIM Loss for Unsupervised Monocular Depth Estimation—0
Structure-Aware Radar-Camera Depth Estimation—0
Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
From Single Images to Motion Policies via Video-Generation Environment Representations—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection—0
Depth Anything with Any Prior—0
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