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 1851–1900 of 2454 papers

TitleStatusHype
Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image—0
PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting LossCode0
A learning-based view extrapolation method for axial super-resolution—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Learning to compose 6-DoF omnidirectional videos using multi-sphere images—0
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image—0
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection—0
S^3: Learnable Sparse Signal Superdensity for Guided Depth Estimation—0
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth EstimationCode0
Vyākarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Towards Continual, Online, Self-Supervised DepthCode0
3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation—0
Progressive Depth Learning for Single Image Dehazing—0
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
LEAD: LiDAR Extender for Autonomous Driving—0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Exploiting Depth Information for Wildlife MonitoringCode0
Deep Event Stereo Leveraged by Event-to-Image Translation—0
Deep Learning--Based Scene Simplification for Bionic VisionCode0
Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum—0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Monocular Depth Estimation for Soft Visuotactile Sensors—0
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge—0
Learning Visual Representation from Human Interactions—0
Single Image Depth Estimation Based on Spectral Consistency and Predicted View—0
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?—0
EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View StereoCode0
Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds—0
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation—0
Unsupervised Simultaneous Depth-from-defocus and Depth-from-focus—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Monocular Depth Parameterizing NetworksCode0
Self-supervised monocular depth estimation from oblique UAV videosCode0
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Multi-Modal Depth Estimation Using Convolutional Neural Networks—0
SimuGAN: Unsupervised forward modeling and optimal design of a LIDAR Camera—0
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation—0
Direct Depth Learning Network for Stereo Matching—0
Robust Consistent Video Depth EstimationCode0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Adaptive Multiplane Image Generation from a Single Internet Picture—0
Space-time Neural Irradiance Fields for Free-Viewpoint Video—0
Variational Monocular Depth Estimation for Reliability Prediction—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