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 1901–1950 of 2454 papers

TitleStatusHype
Real-time single image depth perception in the wild with handheld devicesCode1
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation—0
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry EstimationCode1
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction—0
Robust Learning Through Cross-Task ConsistencyCode1
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Auto-Rectify Network for Unsupervised Indoor Depth EstimationCode1
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding—0
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation—0
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous DrivingCode1
Height and Uprightness Invariance for 3D Prediction From a Single ViewCode1
Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery—0
Disparity-Aware Domain Adaptation in Stereo Image Restoration—0
Online Depth Learning Against Forgetting in Monocular Videos—0
Pattern-Structure Diffusion for Multi-Task Learning—0
Robust Learning Through Cross-Task ConsistencyCode1
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation—0
BiFuse: Monocular 360 Depth Estimation via Bi-Projection FusionCode1
Structure-Guided Ranking Loss for Single Image Depth PredictionCode1
Attention-Aware Multi-View Stereo—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Upgrading Optical Flow to 3D Scene Flow Through Optical ExpansionCode1
VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines—0
Why Having 10,000 Parameters in Your Camera Model Is Better Than TwelveCode1
Fusion of Real Time Thermal Image and 1D/2D/3D Depth Laser Readings for Remote Thermal Sensing in Industrial Plants by Means of UAVs and/or Robots—0
Monocular Depth Estimators: Vulnerabilities and Attacks—0
Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences—0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding—0
Visual Localization Using Semantic Segmentation and Depth Prediction—0
Focus on defocus: bridging the synthetic to real domain gap for depth estimationCode1
Decoder Modulation for Indoor Depth Completion—0
Deep feature fusion for self-supervised monocular depth prediction—0
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data—0
Bi3D: Stereo Depth Estimation via Binary ClassificationsCode1
Taskology: Utilizing Task Relations at Scale—0
On the uncertainty of self-supervised monocular depth estimationCode1
Self-Supervised Human Depth Estimation from Monocular VideosCode1
VisualEchoes: Spatial Image Representation Learning through Echolocation—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Consistent Video Depth EstimationCode2
Deflating Dataset Bias Using Synthetic Data Augmentation—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Self-Supervised Attention Learning for Depth and Ego-motion Estimation—0
Learning to Autofocus—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth PredictionCode0
Robust 3D reconstruction of dynamic scenes from single-photon lidar using Beta-divergences—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for 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