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 1051–1100 of 2454 papers

TitleStatusHype
Learning structure-from-motion from motion—0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift—0
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment—0
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks—0
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation—0
Error Diagnosis of Deep Monocular Depth Estimation Models—0
A Unified Formulation for Visual Odometry—0
Learning Ordinal Relationships for Mid-Level Vision—0
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images—0
Augmenting Depth Estimation with Geospatial Context—0
Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth—0
Entropy-difference based stereo error detection—0
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation—0
CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality—0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems—0
Adversarial View-Consistent Learning for Monocular Depth Estimation—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Cross-View Completion Models are Zero-shot Correspondence Estimators—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications—0
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs—0
Cross-spectral Gated-RGB Stereo Depth Estimation—0
Learning Monocular Depth from Events via Egomotion Compensation—0
Energy-based Domain-Adaptive Segmentation with Depth Guidance—0
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection—0
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN—0
Attentive Feature Reuse for Multi Task Meta learning—0
End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation—0
End-to-end depth from motion with stabilized monocular videos—0
Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video—0
CroMo: Cross-Modal Learning for Monocular Depth Estimation—0
360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion—0
CRF360D: Monocular 360 Depth Estimation via Spherical Fully-Connected CRFs—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video—0
Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion—0
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks—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