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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 301–350 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
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation—0
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures—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
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence—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
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors—0
Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles—0
VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale RecoveryCode0
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment—0
Dense Geometry Supervision for Underwater Depth Estimation—0
Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images—0
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation—0
Occlusion-Ordered Semantic Instance Segmentation—0
An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open WorldCode0
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data GenerationCode0
One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images—0
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces—0
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous DrivingCode0
Radar-Guided Polynomial Fitting for Metric Depth Estimation—0
Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation—0
Multi-view Reconstruction via SfM-guided Monocular Depth Estimation—0
3D Densification for Multi-Map Monocular VSLAM in Endoscopy—0
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images—0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image DecompositionCode0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
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