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 901–925 of 2326 papers

TitleStatusHype
Geometric Scene Parsing with Hierarchical LSTM—0
Geometric Prior-Guided Neural Implicit Surface Reconstruction in the Wild—0
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale—0
An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan—0
3D Reconstruction using Structured Light from off-the-shelf components—0
Globally optimal point set registration by joint symmetry plane fitting—0
Geodesic-HOF: 3D Reconstruction Without Cutting Corners—0
GMM-IKRS: Gaussian Mixture Models for Interpretable Keypoint Refinement and Scoring—0
Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion—0
GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors—0
Continuous 3D Perception Model with Persistent State—0
GenHMR: Generative Human Mesh Recovery—0
GenFusion: Closing the Loop between Reconstruction and Generation via Videos—0
Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects—0
Anatomical basis of sex differences in the electrocardiogram identified by three-dimensional torso-heart imaging reconstruction pipeline—0
3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset—0
Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation—0
Consistent sets of lines with no colorful incidence—0
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images—0
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction—0
Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting—0
Consistent Recurrent Neural Networks for 3D Neuron Segmentation—0
An Approach for Noise Removal on Depth Images—0
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers—0
Show:102550
← PrevPage 37 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