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
Detecting Invisible PeopleCode1
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth EstimationCode1
Learning Sub-Pixel Disparity Distribution for Light Field Depth EstimationCode1
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye CameraCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that MatterCode1
LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth RenderingCode1
FlipNeRF: Flipped Reflection Rays for Few-shot Novel View SynthesisCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
Chitransformer: Towards Reliable Stereo From CuesCode1
Deep Color Mismatch Correction In Stereoscopic 3D ImagesCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth EstimationCode1
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
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
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRICode1
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth EstimationCode1
Deep Depth From FocusCode1
Deeper Depth Prediction with Fully Convolutional Residual NetworksCode1
Self-supervised 3D Representation Learning of Dressed Humans from Social Media VideosCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
A Hybrid Sparse-Dense Monocular SLAM System for Autonomous DrivingCode1
Gated2Gated: Self-Supervised Depth Estimation from Gated ImagesCode1
Channel-Aware Distillation Transformer for Depth Estimation on Nano DronesCode1
Learning Depth Estimation for Transparent and Mirror SurfacesCode1
Learning Depth from Focus in the WildCode1
Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View StereoCode1
Learning Ego 3D Representation as Ray TracingCode1
Learning Light Field Angular Super-Resolution via a Geometry-Aware NetworkCode1
Geometry Uncertainty Projection Network for Monocular 3D Object DetectionCode1
NDDepth: Normal-Distance Assisted Monocular Depth EstimationCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
CDGS: Confidence-Aware Depth Regularization for 3D Gaussian SplattingCode1
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object DetectionCode1
GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsamplingCode1
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
Learnable Differencing Center for Nighttime Depth PerceptionCode1
GroCo: Ground Constraint for Metric Self-Supervised Monocular DepthCode1
Deep Ordinal Regression Network for Monocular Depth EstimationCode1
Depth Map Decomposition for Monocular Depth EstimationCode1
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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