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 651–700 of 2454 papers

TitleStatusHype
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor ScenesCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
FlipNeRF: Flipped Reflection Rays for Few-shot Novel View SynthesisCode1
NDDepth: Normal-Distance Assisted Monocular Depth EstimationCode1
Depth Estimation from Monocular Images and Sparse Radar DataCode1
Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the massesCode1
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view StereoCode1
Net2Brain: A Toolbox to compare artificial vision models with human brain responsesCode1
Domain Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Depth Estimation From Indoor Panoramas With Neural Scene RepresentationCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
Non-Local Spatial Propagation Network for Depth CompletionCode1
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and TrackingCode1
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal EndoscopyCode1
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye CameraCode1
Occlusion-Aware Depth Estimation with Adaptive Normal ConstraintsCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D ScansCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
OmniVidar: Omnidirectional Depth Estimation From Multi-Fisheye ImagesCode1
Depth estimation from 4D light field videosCode1
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
Flare-Free Vision: Empowering Uformer with Depth InsightsCode1
Focus on defocus: bridging the synthetic to real domain gap for depth estimationCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera TrapsCode1
P^2Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth EstimationCode1
FDCT: Fast Depth Completion for Transparent ObjectsCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
PanopticDepth: A Unified Framework for Depth-aware Panoptic SegmentationCode1
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light FieldsCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward EquilibriumCode1
360^ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of IcosahedronCode1
Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth CellsCode1
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation ModelsCode1
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton RefinementCode1
Context-Enhanced Stereo TransformerCode1
Continual Adaptation for Deep StereoCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetryCode1
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth ImageCode1
PLUMENet: Efficient 3D Object Detection from Stereo ImagesCode1
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthCode1
Multi-Loss Rebalancing Algorithm 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