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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–575 of 2454 papers

TitleStatusHype
Cross-spectral Gated-RGB Stereo Depth Estimation—0
Depth Reconstruction with Neural Signed Distance Fields in Structured Light Systems—0
Depth Prompting for Sensor-Agnostic Depth EstimationCode0
CRF360D: Monocular 360 Depth Estimation via Spherical Fully-Connected CRFs—0
Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation ModelsCode1
GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision—0
FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth EstimationCode0
KPNDepth: Depth Estimation of Lane Images under Complex Rainy Environment—0
Towards Task-Compatible Compressible RepresentationsCode0
EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic CameraCode2
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition—0
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks—0
SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling—0
MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth Smooth Regularization—0
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation—0
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose—0
M^2Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation—0
DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular VideosCode3
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Depth Priors in Removal Neural Radiance Fields—0
Tackling water table depth modeling via machine learning: From proxy observations to verifiabilityCode0
Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting—0
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image EnhancementCode1
A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation—0
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images—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