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 101–125 of 2326 papers

TitleStatusHype
Unconstrained Large-scale 3D Reconstruction and Rendering across Altitudes—0
GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D ReconstructionCode2
Mesh-Learner: Texturing Mesh with Spherical HarmonicsCode1
PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision ModelsCode1
Object Learning and Robust 3D Reconstruction—0
Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fields—0
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction—0
VGNC: Reducing the Overfitting of Sparse-view 3DGS via Validation-guided Gaussian Number Control—0
IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays—0
Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction—0
ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos—0
St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World—0
TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors—0
AAA-Gaussians: Anti-Aliased and Artifact-Free 3D Gaussian Rendering—0
SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians—0
Regist3R: Incremental Registration with Stereo Foundation Model—0
Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation—0
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping—0
Deep Learning-based Bathymetry Retrieval without In-situ Depths using Remote Sensing Imagery and SfM-MVS DSMs with Data GapsCode1
Explicit and Implicit Representations in AI-based 3D Reconstruction for Radiology: A Systematic ReviewCode1
TT3D: Table Tennis 3D Reconstruction—0
LL-Gaussian: Low-Light Scene Reconstruction and Enhancement via Gaussian Splatting for Novel View Synthesis—0
smFISH_batchRun: A smFISH image processing tool for single-molecule RNA Detection and 3D reconstruction—0
A Constrained Optimization Approach for Gaussian Splatting from Coarsely-posed Images and Noisy Lidar Point Clouds—0
Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset—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