SOTAVerified

Depth Estimation

Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

Papers

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Showing 551–600 of 2454 papers

TitleStatusHype
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation ModelsCode1
CaFNet: A Confidence-Driven Framework for Radar Camera Depth EstimationCode1
Dual Pixel Exploration: Simultaneous Depth Estimation and Image RestorationCode1
Calibrating Panoramic Depth Estimation for Practical Localization and MappingCode1
Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus SupervisionCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented RealityCode1
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward EquilibriumCode1
Depth Map Decomposition for Monocular Depth EstimationCode1
Camera-based 3D Semantic Scene Completion with Sparse Guidance NetworkCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
Bridging the Gap between Multi-focus and Multi-modal: A Focused Integration Framework for Multi-modal Image FusionCode1
EPI-based Oriented Relation Networks for Light Field Depth EstimationCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetryCode1
Objects are Different: Flexible Monocular 3D Object DetectionCode1
OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth EstimationCode1
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
Occlusion-Aware Depth Estimation with Adaptive Normal ConstraintsCode1
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
Estimating Image Depth in the Comics DomainCode1
MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane SweepsCode1
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and TrackingCode1
Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View GeometryCode1
Domain Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the massesCode1
Multi-view Depth Estimation using Epipolar Spatio-Temporal NetworksCode1
Brain Captioning: Decoding human brain activity into images and textCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
Categorical Depth Distribution Network for Monocular 3D Object DetectionCode1
On the Importance of Accurate Geometry Data for Dense 3D Vision TasksCode1
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-LabelingCode1
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth EstimationCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton RefinementCode1
CDGS: Confidence-Aware Depth Regularization for 3D Gaussian SplattingCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Boundary-induced and scene-aggregated network for monocular depth predictionCode1
Detecting Invisible PeopleCode1
DiverseDepth: Affine-invariant Depth Prediction Using Diverse DataCode1
Multi-View Multi-Person 3D Pose Estimation with Plane Sweep StereoCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
DevNet: Self-supervised Monocular Depth Learning via Density Volume ConstructionCode1
Flare-Free Vision: Empowering Uformer with Depth InsightsCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
Automated Distance Estimation for Wildlife Camera TrappingCode1
Depth Estimation from Monocular Images and Sparse Radar DataCode1
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All datasetsStanford2D3D PanoramicNYU-Depth V2DCMeBDthequeScanNetV2Cityscapes testDIODEKITTI 2015Mars DTM EstimationScanNet4D Light Field DatasetKITTI Eigen split

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniDepthRMSE0.62—Unverified
2SphereDepthRMSE0.45—Unverified
3Jin et al.RMSE0.42—Unverified
4BiFuse with fusionRMSE0.41—Unverified
5HoHoNet (ResNet-101)RMSE0.38—Unverified
6PanoDepthRMSE0.37—Unverified
7BiFuse++RMSE0.37—Unverified
8UniFuse with fusionRMSE0.37—Unverified
9DisConvRMSE0.37—Unverified
10SliceNetRMSE0.37—Unverified
#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61—Unverified
2PAD-NetRMS0.79—Unverified
3MS-CRFRMS0.59—Unverified
4DORNRMS0.51—Unverified
5FreeformRMS0.43—Unverified
6Optimized, freeformRMS0.43—Unverified
7VNLRMS0.42—Unverified
8BTSRMS0.41—Unverified
9TransDepth (AGD+ ViT)RMS0.37—Unverified
10AdaBinsRMS0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.35—Unverified
2MIDASAbs Rel0.31—Unverified
3Bhattacharjee et al.Abs Rel0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.49—Unverified
2MIDASAbs Rel0.42—Unverified
3Bhattacharjee et al.Abs Rel0.38—Unverified
#ModelMetricClaimedVerifiedStatus
1LeReSabsolute relative error0.1—Unverified
2DELTASabsolute relative error0.09—Unverified
3Distill Any Depthabsolute relative error0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1SDC-DepthRMSE6.92—Unverified
2SwinMTLRMSE6.35—Unverified
#ModelMetricClaimedVerifiedStatus
1AIP-BrownDelta < 1.250.36—Unverified
2LeResDelta < 1.250.23—Unverified
#ModelMetricClaimedVerifiedStatus
1H-Net (Ours)Absolute relative error (AbsRel)0.09—Unverified
2H-Net (Ours) Full EigenAbsolute relative error (AbsRel)0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1GLPDepthDelta < 1.250.43—Unverified
2SRDINET (Model A)Delta < 1.250.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)RMSE0.17—Unverified
2Atlas (plain)RMSE0.17—Unverified
#ModelMetricClaimedVerifiedStatus
1LFattNetBadPix(0.01)17.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LightDepthNumber of parameters (M)42.6—Unverified
#ModelMetricClaimedVerifiedStatus
1UniFuseAbs Rel0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error1.63—Unverified