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 401–450 of 2454 papers

TitleStatusHype
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
Estimating Image Depth in the Comics DomainCode1
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth EstimationCode1
LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum LearningCode1
LightedDepth: Video Depth Estimation in Light of Limited Inference View AnglesCode1
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that MatterCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
M4Depth: Monocular depth estimation for autonomous vehicles in unseen environmentsCode1
Event-based Stereo Depth Estimation from Ego-motion using Ray Density FusionCode1
Deep Color Mismatch Correction In Stereoscopic 3D ImagesCode1
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton RefinementCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth EstimationCode1
Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater RobotsCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and ChallengesCode1
Deep Depth Estimation from Visual-Inertial SLAMCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse InputsCode1
Mobile AR Depth Estimation: Challenges & Prospects -- Extended VersionCode1
Deep Depth From FocusCode1
Deeper Depth Prediction with Fully Convolutional Residual NetworksCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Chitransformer: Towards Reliable Stereo From CuesCode1
A Hybrid Sparse-Dense Monocular SLAM System for Autonomous DrivingCode1
Energy-Efficient Adaptive 3D SensingCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
AI Playground: Unreal Engine-based Data Ablation Tool for Deep LearningCode1
Monocular Depth Prediction through Continuous 3D LossCode1
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy LabelsCode1
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion ModelCode1
MonoGround: Detecting Monocular 3D Objects from the GroundCode1
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object DetectionCode1
Channel-Aware Distillation Transformer for Depth Estimation on Nano DronesCode1
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth PredictionCode1
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object DetectionCode1
Efficient Neural Radiance Fields for Interactive Free-viewpoint VideoCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
Deep Multi-View Stereo gone wildCode1
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object DetectionCode1
A Light and Tuning-free Method for Simulating Camera Motion in Video GenerationCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetryCode1
Deep Ordinal Regression Network for Monocular Depth EstimationCode1
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light FieldsCode1
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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