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
A Practical Stereo Depth System for Smart GlassesCode1
InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular DepthCode1
Attention Attention Everywhere: Monocular Depth Prediction with Skip AttentionCode1
Instance-wise Occlusion and Depth Orders in Natural ScenesCode1
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy PredictionCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
Learning Stereo from Single ImagesCode1
Learning Sub-Pixel Disparity Distribution for Light Field Depth EstimationCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
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
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
Depth Map Decomposition for Monocular Depth EstimationCode1
CL-MVSNet: Unsupervised Multi-View Stereo with Dual-Level Contrastive LearningCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented RealityCode1
Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task LearningCode1
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
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
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
Curvature-guided dynamic scale networks for Multi-view StereoCode1
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth PerceptionCode1
CutDepth:Edge-aware Data Augmentation in Depth EstimationCode1
Absolute distance prediction based on deep learning object detection and monocular depth estimation modelsCode1
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information FusionCode1
Improving Deep Regression with Ordinal EntropyCode1
RGB-D Indiscernible Object Counting in Underwater ScenesCode1
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
AeDet: Azimuth-invariant Multi-view 3D Object DetectionCode1
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
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal EstimationCode1
Depth Estimation from Monocular Images and Sparse Radar DataCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Chitransformer: Towards Reliable Stereo From CuesCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
IEBins: Iterative Elastic Bins for Monocular Depth EstimationCode1
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
Image Masking for Robust Self-Supervised Monocular Depth EstimationCode1
Channel-Aware Distillation Transformer for Depth Estimation on Nano DronesCode1
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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