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 1001–1050 of 2454 papers

TitleStatusHype
Collaboration Helps Camera Overtake LiDAR in 3D DetectionCode1
Monocular Visual-Inertial Depth EstimationCode1
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
Fully Self-Supervised Depth Estimation from Defocus ClueCode1
Channel-Aware Distillation Transformer for Depth Estimation on Nano DronesCode1
Spectrum-inspired Low-light Image Translation for Saliency Detection—0
Exploring Sparse Visual Prompt for Domain Adaptive Dense PredictionCode1
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video—0
Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains—0
A Simple Baseline for Supervised Surround-view Depth Estimation—0
Discovering Multiple Algorithm Configurations—0
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth EstimationCode1
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic ScenesCode1
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Aberration-Aware Depth-from-FocusCode1
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
Unleashing Text-to-Image Diffusion Models for Visual PerceptionCode2
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction—0
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model—0
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation—0
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections—0
Monocular Depth Estimation using Diffusion Models—0
Learning to Estimate Two Dense Depths from LiDAR and Event Data—0
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous DrivingCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric DepthCode5
VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene CompletionCode3
MVTrans: Multi-View Perception of Transparent Objects—0
Bokeh Rendering Based on Adaptive Depth Calibration Network—0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
An evaluation of deep learning models for predicting water depth evolution in urban floods—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
Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset RefinementCode0
MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse InputsCode1
Long Range Object-Level Monocular Depth Estimation for UAVs—0
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth EstimationCode1
Spectral 3D Computer Vision -- A Review—0
VA-DepthNet: A Variational Approach to Single Image Depth PredictionCode1
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions—0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
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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