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 2351–2400 of 2454 papers

TitleStatusHype
Leveraging 6DoF Pose Foundation Models For Mapping Marine Sediment BurialCode0
Task-Aware Active Learning for Endoscopic Image AnalysisCode0
Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth EstimationCode0
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
Learning Unsupervised Multi-View Stereopsis via Robust Photometric ConsistencyCode0
AutoColor: Learned Light Power Control for Multi-Color HologramsCode0
Learning to Synthesize a 4D RGBD Light Field from a Single ImageCode0
Learning to Navigate in Complex EnvironmentsCode0
Learning to Adapt for StereoCode0
Learning Single Camera Depth Estimation using Dual-PixelsCode0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Learning Non-Volumetric Depth Fusion Using Successive ReprojectionsCode0
Learning Multi-modal Information for Robust Light Field Depth EstimationCode0
Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset RefinementCode0
Learning monocular depth estimation with unsupervised trinocular assumptionsCode0
EgoDTM: Towards 3D-Aware Egocentric Video-Language PretrainingCode0
Task-guided Domain Gap Reduction for Monocular Depth Prediction in EndoscopyCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene SegmentationCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New DomainsCode0
Self-Supervised Monocular Depth HintsCode0
Convolution kernel adaptation to calibrated fisheyeCode0
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial AttacksCode0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Learning Depth from Single Monocular Images Using Deep Convolutional Neural FieldsCode0
Learning Across Tasks and DomainsCode0
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth EstimationCode0
Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth EstimationCode0
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN ScenesCode0
Tackling water table depth modeling via machine learning: From proxy observations to verifiabilityCode0
Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?Code0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Dynamic Filter NetworksCode0
LCD: Learned Cross-Domain Descriptors for 2D-3D MatchingCode0
The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse WeatherCode0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Joint Depth Estimation and Mixture of Rain Removal From a Single ImageCode0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
Semantic Information in Contrastive LearningCode0
The Devil is in the Decoder: Classification, Regression and GANsCode0
Consensus-based Optimization for 3D Human Pose Estimation in Camera CoordinatesCode0
Dual CNN Models for Unsupervised Monocular Depth EstimationCode0
DPF^*: improved Depth Potential Function for scale-invariant sulcal depth estimationCode0
iToF-flow-based High Frame Rate Depth ImagingCode0
Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous DrivingCode0
On the Viability of Monocular Depth Pre-training for Semantic SegmentationCode0
Investigating Neural Architectures by Synthetic Dataset DesignCode0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
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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