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 2401–2450 of 2454 papers

TitleStatusHype
Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural NetworkCode0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data GenerationCode0
Achieving Risk Control in Online Learning SettingsCode0
Attacking Attention of Foundation Models Disrupts Downstream TasksCode0
Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive PerspectiveCode0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane RepresentationCode0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Unsupervised Adversarial Depth Estimation using Cycled Generative NetworksCode0
Indoor Depth Completion with Boundary Consistency and Self-AttentionCode0
Unsupervised CNN for Single View Depth Estimation: Geometry to the RescueCode0
Digging Into Self-Supervised Monocular Depth EstimationCode0
SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image DecompositionCode0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
Index NetworkCode0
Unsupervised Depth Estimation, 3D Face Rotation and ReplacementCode0
Improving Self-Supervised Single View Depth Estimation by Masking OcclusionCode0
DFR: Depth from Rotation by Uncalibrated Image Rectification with Latitudinal Motion AssumptionCode0
Improving Neural Radiance Fields with Depth-aware Optimization for Novel View SynthesisCode0
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
Unsupervised Domain Adaptation for Depth Prediction from ImagesCode0
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry PerceptionCode0
SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB ImagesCode0
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task ConsistencyCode0
Vision: A Deep Learning Approach to provide walking assistance to the visually impairedCode0
TomatoScanner: phenotyping tomato fruit based on only RGB imageCode0
Improved Depth Estimation of Bayesian Neural NetworksCode0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth EstimationCode0
IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image ModelsCode0
Achievement-Based Training Progress Balancing for Multi-Task LearningCode0
Unsupervised Learning of 3D Scene Flow from Monocular CameraCode0
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
SING: A Plug-and-Play DNN Learning TechniqueCode0
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation TechniquesCode0
Single-Image Depth Estimation Based on Fourier Domain AnalysisCode0
Color-Guided Flying Pixel Correction in Depth ImagesCode0
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inferenceCode0
IA-MVS: Instance-Focused Adaptive Depth Sampling for Multi-View StereoCode0
HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object DetectionCode0
Single-Image Depth Perception in the WildCode0
Detecting Adversarial Perturbations in Multi-Task PerceptionCode0
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality DecoderCode0
How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth ForecastingCode0
Detail-aware multi-view stereo network for depth estimationCode0
Unsupervised Learning of Depth and Ego-Motion from VideoCode0
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric ConstraintsCode0
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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