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

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Showing 2301–2326 of 2326 papers

TitleStatusHype
When 3D Reconstruction Meets Ubiquitous RGB-D Images—0
Learning-by-Synthesis for Appearance-based 3D Gaze Estimation—0
Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement—0
Multi-Output Learning for Camera Relocalization—0
Frequency-Based 3D Reconstruction of Transparent and Specular Objects—0
Single-View 3D Scene Parsing by Attributed Grammar—0
Reflectance and Fluorescent Spectra Recovery based on Fluorescent Chromaticity Invariance under Varying Illumination—0
A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video—0
Dense Reconstruction Using 3D Object Shape Priors—0
Dense 3D Reconstruction from Severely Blurred Images Using a Single Moving Camera—0
Joint 3D Scene Reconstruction and Class Segmentation—0
Learning a Manifold as an Atlas—0
Joint Detection, Tracking and Mapping by Semantic Bundle Adjustment—0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
Tensor-Based Human Body Modeling—0
Relative Volume Constraints for Single View 3D Reconstruction—0
Template-Based Isometric Deformable 3D Reconstruction with Sampling-Based Focal Length Self-Calibration—0
Articulated and Restricted Motion Subspaces and Their Signatures—0
A Theory of Refractive Photo-Light-Path Triangulation—0
Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity—0
ChESS - Quick and Robust Detection of Chess-board FeaturesCode0
Thompson Sampling: An Asymptotically Optimal Finite Time AnalysisCode0
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—0
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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