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 1801–1850 of 2454 papers

TitleStatusHype
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow—0
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
What Can You Learn from Your Muscles? Learning Visual Representation from Human InteractionsCode1
Learning Monocular Dense Depth from EventsCode1
Relative Depth Estimation as a Ranking Problem—0
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation—0
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual RealityCode1
Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical MicroscopyCode1
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionCode0
Joint Pruning & Quantization for Extremely Sparse Neural Networks—0
Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature AdaptationCode1
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection—0
Light Field Compression by Residual CNN Assisted JPEGCode0
Depth Estimation from Monocular Images and Sparse Radar DataCode1
Adaptive confidence thresholding for monocular depth estimationCode1
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Calibrating Self-supervised Monocular Depth Estimation—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
View-consistent 4D Light Field Depth EstimationCode1
Rain rendering for evaluating and improving robustness to bad weather—0
Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas—0
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Multi-Loss Weighting with Coefficient of VariationsCode1
Bidirectional Attention Network for Monocular Depth EstimationCode1
VR-Caps: A Virtual Environment for Capsule EndoscopyCode1
One Shot 3D PhotographyCode1
Single-Image Depth Prediction Makes Feature Matching EasierCode1
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Visibility-aware Multi-view Stereo NetworkCode1
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillationCode1
Self-Supervised Learning for Monocular Depth Estimation from Aerial ImageryCode1
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motionCode2
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
Learning Stereo from Single ImagesCode1
Shape Consistent 2D Keypoint Estimation under Domain Shift—0
MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks—0
S³Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data—0
P²Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth EstimationCode1
Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image—0
Disambiguating Monocular Depth Estimation with a Single Transient—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—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