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 1851–1900 of 2454 papers

TitleStatusHype
Evaluation of CNN-based Single-Image Depth Estimation Methods—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions—0
Event-based Monocular Dense Depth Estimation with Recurrent Transformers—0
Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns—0
Event-based tracking of human hands—0
Event fields: Capturing light fields at high speed, resolution, and dynamic range—0
Event Guided Depth Sensing—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds—0
Event Transformer+. A multi-purpose solution for efficient event data processing—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media—0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More—0
Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding—0
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation—0
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Exploring Deep Spiking Neural Networks for Automated Driving Applications—0
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data—0
ExScene: Free-View 3D Scene Reconstruction with Gaussian Splatting from a Single Image—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
FaDIV-Syn: Fast Depth-Independent View Synthesis using Soft Masks and Implicit Blending—0
FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery—0
Fast and Accurate Depth Estimation from Sparse Light Fields—0
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields—0
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report—0
Fast and Efficient Lenslet Image Compression—0
Fast camera focus estimation for gaze-based focus control—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Fast Underwater Scene Reconstruction using Multi-View Stereo and Physical Imaging—0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training—0
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation—0
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation—0
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation—0
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments—0
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs—0
Flexible Depth Completion for Sparse and Varying Point Densities—0
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular 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