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 1701–1750 of 2454 papers

TitleStatusHype
Scale-aware direct monocular odometry—0
Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields—0
Scaling 4D Representations—0
Scaling Properties of Diffusion Models for Perceptual Tasks—0
Scene Depth Estimation from Traditional Oriental Landscape Paintings—0
SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling—0
SceneScape: Text-Driven Consistent Scene Generation—0
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation—0
SDGE: Stereo Guided Depth Estimation for 360^ Camera Sets—0
SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing—0
SDNet: Semantically Guided Depth Estimation Network—0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance—0
Seeing and Seeing Through the Glass: Real and Synthetic Data for Multi-Layer Depth Estimation—0
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection—0
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction—0
Selective Feature Adapter for Dense Vision Transformers—0
Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences—0
Self-Guided Instance-Aware Network for Depth Completion and Enhancement—0
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR—0
Self-Supervised Attention Learning for Depth and Ego-motion Estimation—0
Self-Supervised Camera Self-Calibration from Video—0
Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos—0
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy—0
Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning—0
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency—0
Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images—0
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues—0
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy—0
Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach—0
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
Self-Supervised Learning of Depth and Camera Motion from 360° Videos—0
Self-Supervised Learning of Domain Invariant Features for Depth Estimation—0
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images—0
Self-supervised Monocular Depth Estimation with Large Kernel Attention—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation—0
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior—0
Self-Supervised Monocular Depth Underwater—0
Self-Supervised Monocular Image Depth Learning and Confidence Estimation—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera—0
Self-supervised Object Motion and Depth Estimation from Video—0
Self-supervised Pretraining and Finetuning for Monocular Depth and Visual Odometry—0
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery—0
Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus—0
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning—0
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and 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