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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 851–876 of 876 papers

TitleStatusHype
Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous DrivingCode0
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread FunctionsCode0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Learning monocular depth estimation with unsupervised trinocular assumptionsCode0
Digging Into Self-Supervised Monocular Depth EstimationCode0
Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural NetworkCode0
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Depth Prompting for Sensor-Agnostic Depth EstimationCode0
SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image DecompositionCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry PerceptionCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Learning Across Tasks and DomainsCode0
Single View Stereo MatchingCode0
Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive PerspectiveCode0
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular VideosCode0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown CamerasCode0
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth EstimationCode0
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy MapsCode0
VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale RecoveryCode0
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
InfraParis: A multi-modal and multi-task autonomous driving datasetCode0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
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