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 776–800 of 2454 papers

TitleStatusHype
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and CompletionCode1
MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable UncertaintyCode1
PolyMaX: General Dense Prediction with Mask Transformer—0
Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images—0
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images—0
Analysis of NaN Divergence in Training Monocular Depth Estimation Model—0
TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene UnderstandingCode1
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Bridging the Gap between Multi-focus and Multi-modal: A Focused Integration Framework for Multi-modal Image FusionCode1
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
Joint Depth Prediction and Semantic Segmentation with Multi-View SAM—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
Learning depth from monocular video sequences—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater RobotsCode1
Towards Explainability in Monocular Depth Estimation—0
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X DataCode1
RoboDepth: Robust Out-of-Distribution Depth Estimation under CorruptionsCode2
Guidance system for Visually Impaired Persons using Deep Learning and Optical flow—0
A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video—0
Mobile AR Depth Estimation: Challenges & Prospects -- Extended VersionCode1
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement—0
Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs—0
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion—0
FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack—0
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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