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 626–650 of 2326 papers

TitleStatusHype
An Illumination-Robust Feature Extractor Augmented by Relightable 3D Reconstruction—0
Calculating Pose with Vanishing Points of Visual-Sphere Perspective Model—0
EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting—0
End-to-end 3D shape inverse rendering of different classes of objects from a single input image—0
Cryo-Electron Ptychography: Applications and Potential in Biological Characterisation—0
AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction—0
Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D Street View Reconstruction—0
CrossSDF: 3D Reconstruction of Thin Structures From Cross-Sections—0
AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video—0
3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images—0
A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling—0
CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants—0
Critical Configurations For Radial Distortion Self-Calibration—0
3D Reconstruction with Generalizable Neural Fields using Scene Priors—0
EndoGaussians: Single View Dynamic Gaussian Splatting for Deformable Endoscopic Tissues Reconstruction—0
Creating a Digital Twin of Spinal Surgery: A Proof of Concept—0
An Error Detection and Correction Framework for Connectomics—0
CountingFruit: Real-Time 3D Fruit Counting with Language-Guided Semantic Gaussian Splatting—0
3D Reconstruction with Fast Dipole Sums—0
3D Instance Segmentation via Multi-Task Metric Learning—0
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise _p Norm—0
Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies—0
What Do Single-view 3D Reconstruction Networks Learn?—0
Corner Detection Based on Multi-directional Gabor Filters with Multi-scales—0
An Efficient Planar Bundle Adjustment Algorithm—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