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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 451–500 of 876 papers

TitleStatusHype
The Third Monocular Depth Estimation Challenge—0
Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation—0
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?Code0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior—0
Adaptive Discrete Disparity Volume for Self-supervised Monocular Depth Estimation—0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
F^2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis—0
Track Everything Everywhere Fast and Robustly—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Language-Based Depth Hints for Monocular Depth Estimation—0
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation—0
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications—0
Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting—0
METER: a mobile vision transformer architecture for monocular depth estimationCode0
WaveShot: A Compact Portable Unmanned Surface Vessel for Dynamic Water Surface Videography and Media Production—0
D4D: An RGBD diffusion model to boost monocular depth estimationCode0
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy—0
Pyramid Feature Attention Network for Monocular Depth Prediction—0
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps—0
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios—0
An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models—0
MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction—0
CLIP Can Understand Depth—0
Diffusion-based Light Field Synthesis—0
Depth Anything in Medical Images: A Comparative Study—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency—0
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes—0
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—0
From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior—0
GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding—0
Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Camera-Independent Single Image Depth Estimation from Defocus BlurCode0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
PolyMaX: General Dense Prediction with Mask Transformer—0
Analysis of NaN Divergence in Training Monocular Depth Estimation Model—0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
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