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 1126–1150 of 2454 papers

TitleStatusHype
Convolutional neural network-based regression for depth prediction in digital holography—0
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
Edge-aware Consistent Stereo Video Depth Estimation—0
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer—0
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues—0
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Dynamic Fusion Network For Light Field Depth Estimation—0
Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR—0
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift—0
Dyna-DepthFormer: Multi-frame Transformer for Self-Supervised Depth Estimation in Dynamic Scenes—0
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Adversarial Attacks on Monocular Depth Estimation—0
Show:102550
← PrevPage 46 of 99Next →
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