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 1951–2000 of 2454 papers

TitleStatusHype
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Monocular Depth Estimation with Self-supervised Instance Adaptation—0
Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving ApplicationsCode1
Self-Supervised Monocular Scene Flow EstimationCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Guiding Monocular Depth Estimation Using Depth-Attention VolumeCode1
Towards Better Generalization: Joint Depth-Pose Learning without PoseNetCode1
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera—0
Occlusion-Aware Depth Estimation with Adaptive Normal ConstraintsCode1
The Edge of Depth: Explicit Constraints between Segmentation and DepthCode1
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity VolumeCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation LearningCode1
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton RefinementCode1
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
High-Accuracy Facial Depth Models derived from 3D Synthetic Data—0
Holopix50k: A Large-Scale In-the-wild Stereo Image DatasetCode1
Adversarial Attacks on Monocular Depth Estimation—0
Atlas: End-to-End 3D Scene Reconstruction from Posed ImagesCode2
Monocular Depth Prediction through Continuous 3D LossCode1
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems—0
Active Depth Estimation: Stability Analysis and its Applications—0
Fast Depth Estimation for View SynthesisCode0
Monocular Depth Estimation Based On Deep Learning: An Overview—0
Softmax Splatting for Video Frame InterpolationCode1
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation—0
Uncertainty depth estimation with gated images for 3D reconstruction—0
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
Scalable Uncertainty for Computer Vision with Functional Variational Inference—0
Harnessing Multi-View Perspective of Light Fields for Low-Light ImagingCode0
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth EstimationCode1
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D GeometryCode1
Learning Depth With Very Sparse Supervision—0
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement FieldsCode1
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthCode2
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
Learning Light Field Angular Super-Resolution via a Geometry-Aware NetworkCode1
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization—0
Attention-based View Selection Networks for Light-field Disparity EstimationCode1
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation—0
DiverseDepth: Affine-invariant Depth Prediction Using Diverse DataCode1
Detecting Deficient Coverage in Colonoscopies—0
Active Perception with A Monocular Camera for Multiscopic VisionCode1
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
Towards Augmented Reality-based Suturing in Monocular Laparoscopic Training—0
Indoor Layout Estimation by 2D LiDAR and Camera Fusion—0
Single Image Depth Estimation Trained via Depth from Defocus CuesCode1
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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