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 2251–2300 of 2454 papers

TitleStatusHype
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information—0
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?Code0
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task ConsistencyCode0
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks—0
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement—0
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss—0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN—0
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation—0
Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement—0
A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation—0
Rethinking Monocular Depth Estimation with Adversarial Training—0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic ImageryCode0
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs—0
DeepTAM: Deep Tracking and MappingCode0
Learning monocular depth estimation with unsupervised trinocular assumptionsCode0
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation TasksCode0
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation NetworkCode0
Geo-Supervised Visual Depth PredictionCode0
Unsupervised Adversarial Depth Estimation using Cycled Generative NetworksCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks—0
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth PredictionCode0
MVDepthNet: Real-time Multiview Depth Estimation Neural NetworkCode0
Peeking Behind Objects: Layered Depth Prediction from a Single Image—0
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs—0
Deep attention-based classification network for robust depth predictionCode0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy—0
Learning Single-Image Depth from Videos using Quality Assessment Networks—0
Deep multi-scale architectures for monocular depth estimation—0
Soccer on Your Tabletop—0
Monocular Depth Estimation with Augmented Ordinal Depth Relationships—0
Free Supervision From Video Games—0
Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling—0
Monocular Relative Depth Perception With Web Stereo Data Supervision—0
Matching Adversarial Networks—0
Single-Image Depth Estimation Based on Fourier Domain AnalysisCode0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
Adversarial Structure Matching for Structured Prediction TasksCode0
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video—0
Auxiliary Tasks in Multi-task LearningCode0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing—0
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors—0
Position Estimation of Camera Based on Unsupervised Learning—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