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 1401–1450 of 2454 papers

TitleStatusHype
Depth-Relative Self Attention for Monocular Depth Estimation—0
Exploring the Mutual Influence between Self-Supervised Single-Frame and Multi-Frame Depth EstimationCode0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
DarSwin: Distortion Aware Radial Swin Transformer—0
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection—0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation—0
The Second Monocular Depth Estimation Challenge—0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
Event-based tracking of human hands—0
Improving Neural Radiance Fields with Depth-aware Optimization for Novel View SynthesisCode0
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—0
EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation—0
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation—0
Joint Depth Estimation and Mixture of Rain Removal From a Single ImageCode0
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take—0
TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions—0
Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes—0
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates—0
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation—0
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions—0
Versatile Depth Estimator Based on Common Relative Depth Estimation and Camera-Specific Relative-to-Metric Depth Conversion—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
Spectrum-inspired Low-light Image Translation for Saliency Detection—0
Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video—0
A Simple Baseline for Supervised Surround-view Depth Estimation—0
Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains—0
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
Discovering Multiple Algorithm Configurations—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model—0
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction—0
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections—0
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation—0
Monocular Depth Estimation using Diffusion Models—0
Learning to Estimate Two Dense Depths from LiDAR and Event Data—0
MVTrans: Multi-View Perception of Transparent Objects—0
Bokeh Rendering Based on Adaptive Depth Calibration Network—0
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images—0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation—0
On the Metrics for Evaluating Monocular Depth Estimation—0
An evaluation of deep learning models for predicting water depth evolution in urban floods—0
Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset RefinementCode0
Long Range Object-Level Monocular Depth Estimation for UAVs—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