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
Depth Estimation From Indoor Panoramas With Neural Scene RepresentationCode1
ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic FusionCode1
Efficient Depth Estimation for Unstable Stereo Camera Systems on AR GlassesCode1
Efficient Neural Radiance Fields for Interactive Free-viewpoint VideoCode1
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement FieldsCode1
Depth estimation from 4D light field videosCode1
Learnable Differencing Center for Nighttime Depth PerceptionCode1
Creating a Forensic Database of Shoeprints from Online Shoe Tread PhotosCode1
Domain Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive LossCode1
Learning an Efficient Multimodal Depth Completion ModelCode1
Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View StereoCode1
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
Learning Ego 3D Representation as Ray TracingCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
A Study on the Generality of Neural Network Structures for Monocular Depth EstimationCode1
Single Image Depth Prediction with Wavelet DecompositionCode1
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
Learning Monocular Dense Depth from EventsCode1
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth PredictionCode1
Predict to Detect: Prediction-guided 3D Object Detection using Sequential ImagesCode1
FourierNets enable the design of highly non-local optical encoders for computational imagingCode1
Learning optical flow from still imagesCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Edge-Enabled VIO with Long-Tracked Features for High-Accuracy Low-Altitude IoT NavigationCode1
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic ScenesCode1
Learning Sub-Pixel Disparity Distribution for Light Field Depth EstimationCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarizationCode1
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light FieldsCode1
LED: Light Enhanced Depth Estimation at NightCode1
Mobile AR Depth Estimation: Challenges & Prospects -- Extended VersionCode1
Dual Pixel Exploration: Simultaneous Depth Estimation and Image RestorationCode1
ConsistNet: Enforcing 3D Consistency for Multi-view Images DiffusionCode1
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward EquilibriumCode1
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic SegmentationCode1
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
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
Context-Enhanced Stereo TransformerCode1
Continual Adaptation for Deep StereoCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Atlantis: Enabling Underwater Depth Estimation with Stable DiffusionCode1
LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum LearningCode1
360^ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of IcosahedronCode1
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth EstimationCode1
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth PerceptionCode1
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded SystemsCode1
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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