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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2301–2350 of 2454 papers

TitleStatusHype
Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe—0
Neural 360^ Structured Light with Learned Metasurfaces—0
Neural Camera Models—0
NeuralLift-360: Lifting an In-the-Wild 2D Photo to a 3D Object With 360deg Views—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry—0
Neural Window Fully-Connected CRFs for Monocular Depth Estimation—0
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error—0
Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field—0
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching—0
Non-local Recurrent Regularization Networks for Multi-view Stereo—0
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
NRMVS: Non-Rigid Multi-View Stereo—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Object-Aware Centroid Voting for Monocular 3D Object Detection—0
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion—0
Objects as Spatio-Temporal 2.5D points—0
OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction—0
Occlusion-Aware Depth Estimation Using Light-Field Cameras—0
Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images—0
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality—0
Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field—0
Occlusion-Ordered Semantic Instance Segmentation—0
OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images—0
OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios—0
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches—0
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments—0
Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving—0
Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding—0
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video—0
Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction—0
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems—0
On depth prediction for autonomous driving using self-supervised learning—0
On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events—0
One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception—0
One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression—0
One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images—0
Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains—0
Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM—0
Online Adaptation through Meta-Learning for Stereo Depth Estimation—0
Online Depth Learning Against Forgetting in Monocular Videos—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
On the Metrics for Evaluating Monocular Depth Estimation—0
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
On the Uncertain Single-View Depths in Colonoscopies—0
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation—0
Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving—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