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 851–875 of 2454 papers

TitleStatusHype
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Deep Classification Network for Monocular Depth Estimation—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces—0
FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack—0
Fast Underwater Scene Reconstruction using Multi-View Stereo and Physical Imaging—0
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
GSGTrack: Gaussian Splatting-Guided Object Pose Tracking from RGB Videos—0
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Fractal Pyramid Networks—0
Guidance system for Visually Impaired Persons using Deep Learning and Optical flow—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions—0
FreSca: Unveiling the Scaling Space in Diffusion Models—0
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction—0
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation—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