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 251–300 of 2454 papers

TitleStatusHype
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height EstimationCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
AudioEar: Single-View Ear Reconstruction for Personalized Spatial AudioCode1
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward EquilibriumCode1
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth EstimationCode1
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor ScenesCode1
Aberration-Aware Depth-from-FocusCode1
Depth Attention for Robust RGB TrackingCode1
Gated2Gated: Self-Supervised Depth Estimation from Gated ImagesCode1
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional NetworkCode1
Densely Constrained Depth Estimator for Monocular 3D Object DetectionCode1
Fully Self-Supervised Depth Estimation from Defocus ClueCode1
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X DataCode1
Attention-based View Selection Networks for Light-field Disparity EstimationCode1
A benchmark with decomposed distribution shifts for 360 monocular depth estimationCode1
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRICode1
GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic ConstraintsCode1
Depth and DOF Cues Make A Better Defocus Blur DetectorCode1
Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive LearningCode1
Depth-Aware Endoscopic Video InpaintingCode1
Attention Attention Everywhere: Monocular Depth Prediction with Skip AttentionCode1
A Benchmark and a Baseline for Robust Multi-view Depth EstimationCode1
Dense Depth Estimation from Multiple 360-degree Images Using Virtual DepthCode1
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
GEDepth: Ground Embedding for Monocular Depth EstimationCode1
Atlantis: Enabling Underwater Depth Estimation with Stable DiffusionCode1
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth ImageCode1
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehiclesCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
Defocus Blur Detection via Depth DistillationCode1
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation LearningCode1
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier ConvolutionsCode1
A Study on the Generality of Neural Network Structures for Monocular Depth EstimationCode1
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal EndoscopyCode1
FlipNeRF: Flipped Reflection Rays for Few-shot Novel View SynthesisCode1
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal FusionCode1
4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free ImagesCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
360MonoDepth: High-Resolution 360° Monocular Depth EstimationCode1
Deep Two-View Structure-from-Motion RevisitedCode1
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP BenchmarkCode1
3DVNet: Multi-View Depth Prediction and Volumetric RefinementCode1
Deep Ordinal Regression Network for Monocular Depth EstimationCode1
Depth-aware Test-Time Training for Zero-shot Video Object SegmentationCode1
Focus on defocus: bridging the synthetic to real domain gap for depth estimationCode1
Frequency-Aware Self-Supervised Monocular Depth EstimationCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Adaptive Surface Normal Constraint for Depth EstimationCode1
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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