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 1551–1575 of 2326 papers

TitleStatusHype
Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications—0
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple ImagesCode1
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling—0
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category ReconstructionCode2
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension—0
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations—0
X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph—0
Domain Adaptation for Real-World Single View 3D Reconstruction—0
Black-Box Test-Time Shape REFINEment for Single View 3D Reconstruction—0
Burst Imaging for Light-Constrained Structure-From-Motion—0
SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D ReconstructionCode0
3D Reconstruction from public webcams—0
A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces—0
Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment ReconstructionCode1
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction—0
Pixel-Perfect Structure-from-Motion with Featuremetric RefinementCode1
Visual Enhanced 3D Point Cloud Reconstruction from A Single Image—0
Towards unconstrained joint hand-object reconstruction from RGB videosCode1
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing—0
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes—0
A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor ScenesCode1
AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo NetworkCode1
3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning—0
Object Wake-up: 3D Object Rigging from a Single Image—0
Shadow Art Revisited: A Differentiable Rendering Based Approach—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