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 1701–1725 of 2326 papers

TitleStatusHype
DeepMesh: Differentiable Iso-Surface Extraction—0
Residential Floor Plan Recognition and Reconstruction—0
Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification—0
Self-Supervised 3D Mesh Reconstruction From Single Images—0
3D Video Stabilization With Depth Estimation by CNN-Based Optimization—0
S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation—0
D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images—0
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey—0
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers—0
GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger—0
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction—0
Automatic 2D-3D Registration without Contrast Agent during Neurovascular Interventions—0
Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar—0
Reconstructing Small 3D Objects in front of a Textured Background—0
Drone-based AI and 3D Reconstruction for Digital Twin Augmentation—0
StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects—0
Galois/monodromy groups for decomposing minimal problems in 3D reconstruction—0
Using Adaptive Gradient for Texture Learning in Single-View 3D Reconstruction—0
D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning—0
Reconstructing 3D Model from Single-View Sketch with Deep Neural Network—0
Computer vision in automated parking systems: Design, implementation and challenges—0
Self-optimizing loop sifting and majorization for 3D reconstruction—0
Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs—0
Soft Expectation and Deep Maximization for Image Feature DetectionCode0
Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video—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