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 2301–2350 of 2454 papers

TitleStatusHype
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network ApproachCode0
Deep Depth Completion of a Single RGB-D ImageCode0
Unsupervised Depth Estimation, 3D Face Rotation and ReplacementCode0
Pyramid Stereo Matching NetworkCode1
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
Monocular Depth Estimation by Learning from Heterogeneous Datasets—0
Robust Depth Estimation from Auto Bracketed Images—0
Fusion of stereo and still monocular depth estimates in a self-supervised learning context—0
Deep Component Analysis via Alternating Direction Neural NetworksCode0
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision—0
Self-Supervised Monocular Image Depth Learning and Confidence Estimation—0
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature ReconstructionCode0
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation—0
Single View Stereo MatchingCode0
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation—0
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep NetworksCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric ConstraintsCode0
Convolutional neural network-based regression for depth prediction in digital holography—0
Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization—0
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Size-to-depth: A New Perspective for Single Image Depth Estimation—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Joint Blind Motion Deblurring and Depth Estimation of Light Field—0
Entropy-difference based stereo error detection—0
Total Variation-Based Dense Depth from Multi-Camera Array—0
Aperture Supervision for Monocular Depth Estimation—0
Depth Assisted Full Resolution Network for Single Image-based View Synthesis—0
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training—0
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction—0
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency—0
Fast camera focus estimation for gaze-based focus control—0
Toward Depth Estimation Using Mask-Based Lensless Cameras—0
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy—0
An Adaptive Framework for Missing Depth Inference Using Joint Bilateral Filter—0
Depth estimation using structured light flow -- analysis of projected pattern flow on an object's surface --—0
Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields—0
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs—0
Depth Estimation Using Structured Light Flow -- Analysis of Projected Pattern Flow on an Object's Surface—0
Depth and Image Restoration From Light Field in a Scattering Medium—0
Monocular 3D Human Pose Estimation by Predicting Depth on Joints—0
Composite Focus Measure for High Quality Depth Maps—0
Low Compute and Fully Parallel Computer Vision With HashMatch—0
Robust Pseudo Random Fields for Light-Field Stereo Matching—0
J-MOD^2: Joint Monocular Obstacle Detection and Depth Estimation—0
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single ImageCode0
Estimated Depth Map Helps Image ClassificationCode0
Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding—0
Automatic Discovery and Geotagging of Objects from Street View ImageryCode0
A Compromise Principle in Deep 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