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 1151–1175 of 2454 papers

TitleStatusHype
Dual Exposure Stereo for Extended Dynamic Range 3D Imaging—0
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
Consistent Depth Prediction under Various Illuminations using Dilated Cross Attention—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Consistent Depth Prediction for Transparent Object Reconstruction from RGB-D Camera—0
Enhanced Depth Estimation and 3D Geometry Reconstruction using Bayesian Helmholtz Stereopsis with Belief Propagation—0
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning—0
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration—0
Consistent Depth of Moving Objects in Video—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
5D Light Field Synthesis from a Monocular Video—0
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation—0
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
DreamCube: 3D Panorama Generation via Multi-plane Synchronization—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
LightDepth: Single-View Depth Self-Supervision from Illumination Decline—0
Light Field Super-Resolution Via Graph-Based Regularization—0
DPS-Net: Deep Polarimetric Stereo Depth Estimation—0
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
DPBridge: Latent Diffusion Bridge for Dense Prediction—0
Gaussian Mixture based Evidential Learning for Stereo Matching—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