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 851–875 of 2454 papers

TitleStatusHype
Learning to Upsample by Learning to SampleCode1
R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras—0
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation—0
Calibrating Panoramic Depth Estimation for Practical Localization and MappingCode1
Depth self-supervision for single image novel view synthesisCode0
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation—0
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)—0
WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters—0
A step towards understanding why classification helps regressionCode0
LightDepth: Single-View Depth Self-Supervision from Illumination Decline—0
Real-time Monocular Depth Estimation on Embedded Systems—0
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization—0
Robust Monocular Depth Estimation under Challenging Conditions—0
MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation—0
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth intervalCode1
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
Out-of-Distribution Detection for Monocular Depth EstimationCode1
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook—0
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution NetworkCode1
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models—0
Blur aware metric depth estimation with multi-focus plenoptic camerasCode1
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—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