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 1001–1025 of 2326 papers

TitleStatusHype
Energy Based Multi-model Fitting & Matching for 3D Reconstruction—0
BRUM: Robust 3D Vehicle Reconstruction from 360 Sparse Images—0
Deep Event Stereo Leveraged by Event-to-Image Translation—0
IM360: Textured Mesh Reconstruction for Large-scale Indoor Mapping with 360^ Cameras—0
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance Fields—0
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation—0
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes—0
Image-based underwater 3D reconstruction for Cultural Heritage: from image collection to 3D. Critical steps and considerations—0
End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response—0
Image compression optimized for 3D reconstruction by utilizing deep neural networks—0
Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks—0
Deep Learned Full-3D Object Completion from Single View—0
Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation—0
Bring Your Rear Cameras for Egocentric 3D Human Pose Estimation—0
End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images—0
End-to-end 3D shape inverse rendering of different classes of objects from a single input image—0
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
Improved Descriptors for Patch Matching and Reconstruction—0
Improving 3D Finger Traits Recognition via Generalizable Neural Rendering—0
CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference—0
Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development—0
Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering—0
IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays—0
Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction—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