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 1376–1400 of 2326 papers

TitleStatusHype
On the generalization of learning-based 3D reconstruction—0
Uncertainty depth estimation with gated images for 3D reconstruction—0
OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction—0
Open-NeRF: Towards Open Vocabulary NeRF Decomposition—0
Open-World Amodal Appearance Completion—0
Optical Diffraction Tomography based on 3D Physics-Inspired Neural Network (PINN)—0
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields—0
Optimal mechanical operation in the vicinity of curved vasculature—0
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette—0
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction—0
Oral-3D: Reconstructing the 3D Bone Structure of Oral Cavity from 2D Panoramic X-ray—0
Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation—0
Orbital Polarimetric Tomography of a Flare Near the Sagittarius A* Supermassive Black Hole—0
ORBSLAM-based Endoscope Tracking and 3D Reconstruction—0
Oriented Light-Field Windows for Scene Flow—0
3D Reconstruction in the Presence of Glasses by Acoustic and Stereo Fusion—0
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics—0
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields—0
3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning—0
Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network—0
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images—0
PanSt3R: Multi-view Consistent Panoptic Segmentation—0
Part123: Part-aware 3D Reconstruction from a Single-view Image—0
Part^2GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting—0
Unconstrained Large-scale 3D Reconstruction and Rendering across Altitudes—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