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 1201–1225 of 2454 papers

TitleStatusHype
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation—0
Deep multi-scale architectures for monocular depth estimation—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting—0
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models—0
From Single Images to Motion Policies via Video-Generation Environment Representations—0
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding—0
DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery—0
A learning-based view extrapolation method for axial super-resolution—0
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning—0
From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images—0
From Image to Video: An Empirical Study of Diffusion Representations—0
DeepLens: Shallow Depth Of Field From A Single Image—0
From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior—0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction—0
FreSca: Unveiling the Scaling Space in Diffusion Models—0
Free Supervision From Video Games—0
Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data—0
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation—0
Fractal Pyramid Networks—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
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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