SOTAVerified

3D Reconstruction

3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.

Image: Gwak et al

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2026–2050 of 2326 papers

TitleStatusHype
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Subsurface Scattering for 3D Gaussian Splatting—0
Success Probability in Multi-View Imaging—0
Deep Depth Completion from Extremely Sparse Data: A Survey—0
A LBP Based Correspondence Identification Scheme for Multi-view Sensing Network—0
Deep Event Stereo Leveraged by Event-to-Image Translation—0
SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition—0
DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions—0
SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite—0
Deep Learned Full-3D Object Completion from Single View—0
Deep-Learning Assisted High-Resolution Binocular Stereo Depth Reconstruction—0
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development—0
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction—0
Deep learning based workflow for accelerated industrial X-ray Computed Tomography—0
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey—0
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views—0
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds—0
Deep Learning in Medical Ultrasound Image Segmentation: a Review—0
Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner—0
Deep Learning Reforms Image Matching: A Survey and Outlook—0
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference—0
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction—0
Show:102550
← PrevPage 82 of 94Next →
All datasetsDTUShapeNetData3D−R2N2Scan2CAD3DPeopleAria Digital Twin DatasetAria Synthetic EnvironmentsScanNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
13D-R2N2Overall0.63—Unverified
2GipumaOverall0.58—Unverified
3COLMAPOverall0.53—Unverified
4MVSNetOverall0.46—Unverified
5Vis-MVSNetOverall0.37—Unverified
6AA-RMVSNetOverall0.36—Unverified
7Cas-MVSNetOverall0.36—Unverified
8EPP-MVSNetOverall0.36—Unverified
9PatchmatchNetOverall0.35—Unverified
10CVP-MVSNetOverall0.35—Unverified
#ModelMetricClaimedVerifiedStatus
1MD-GONIoU92.8—Unverified
2POCOIoU92.6—Unverified
3FS-SDFIoU91.2—Unverified
4DP-ConvONetIoU89.5—Unverified
5ConvONetIoU88.4—Unverified
6ONetIoU76.1—Unverified
7EVolTIoU73.8—Unverified
8ZubicLioIoU65.43—Unverified
#ModelMetricClaimedVerifiedStatus
1AttSets3DIoU0.64—Unverified
2PSGN3DIoU0.64—Unverified
3OGN3DIoU0.6—Unverified
43D-R2N23DIoU0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Scan2CADAverage Accuracy31.68—Unverified
23DMatchAverage Accuracy10.29—Unverified
#ModelMetricClaimedVerifiedStatus
1SVCPChamfer10—Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy18.2—Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy5.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)3DIoU89.4—Unverified