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 351–400 of 2454 papers

TitleStatusHype
EPI-based Oriented Relation Networks for Light Field Depth EstimationCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Energy-Efficient Adaptive 3D SensingCode1
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth intervalCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Geometry Uncertainty Projection Network for Monocular 3D Object DetectionCode1
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal EstimationCode1
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal EstimationCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
A benchmark with decomposed distribution shifts for 360 monocular depth estimationCode1
Attention-based View Selection Networks for Light-field Disparity EstimationCode1
Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth LearningCode1
Gradient-based Uncertainty for Monocular Depth EstimationCode1
GroCo: Ground Constraint for Metric Self-Supervised Monocular DepthCode1
Guiding Monocular Depth Estimation Using Depth-Attention VolumeCode1
3D Visual Illusion Depth EstimationCode1
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy LabelsCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal FeaturesCode1
Deep Color Mismatch Correction In Stereoscopic 3D ImagesCode1
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth EstimationCode1
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task LearningCode1
AudioEar: Single-View Ear Reconstruction for Personalized Spatial AudioCode1
A Practical Stereo Depth System for Smart GlassesCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
Curvature-guided dynamic scale networks for Multi-view StereoCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Absolute distance prediction based on deep learning object detection and monocular depth estimation modelsCode1
Estimating Image Depth in the Comics DomainCode1
Efficient Neural Radiance Fields for Interactive Free-viewpoint VideoCode1
CL-MVSNet: Unsupervised Multi-View Stereo with Dual-Level Contrastive LearningCode1
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth PerceptionCode1
InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular DepthCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Edge-Enabled VIO with Long-Tracked Features for High-Accuracy Low-Altitude IoT NavigationCode1
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation ModelCode1
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth AdaptationCode1
IntroductionCode1
Joint Learning of Salient Object Detection, Depth Estimation and Contour ExtractionCode1
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving ScenesCode1
Efficient Depth Estimation for Unstable Stereo Camera Systems on AR GlassesCode1
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Learnable Differencing Center for Nighttime Depth PerceptionCode1
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light FieldsCode1
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth PredictionCode1
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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