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 1001–1025 of 2454 papers

TitleStatusHype
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation—0
Monocular Visual-Inertial Depth EstimationCode1
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions—0
Versatile Depth Estimator Based on Common Relative Depth Estimation and Camera-Specific Relative-to-Metric Depth Conversion—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
Fully Self-Supervised Depth Estimation from Defocus ClueCode1
Channel-Aware Distillation Transformer for Depth Estimation on Nano DronesCode1
Exploring Sparse Visual Prompt for Domain Adaptive Dense PredictionCode1
Spectrum-inspired Low-light Image Translation for Saliency Detection—0
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video—0
A Simple Baseline for Supervised Surround-view Depth Estimation—0
Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains—0
Discovering Multiple Algorithm Configurations—0
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth EstimationCode1
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Aberration-Aware Depth-from-FocusCode1
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic ScenesCode1
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
Unleashing Text-to-Image Diffusion Models for Visual PerceptionCode2
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction—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