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
MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving—0
Coordinate Quantized Neural Implicit Representations for Multi-view ReconstructionCode1
TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo—0
A Theory of Topological Derivatives for Inverse Rendering of Geometry—0
Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos—0
DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume ReconstructionCode1
O^2-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion ModelCode1
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction—0
Long-Range Grouping Transformer for Multi-View 3D ReconstructionCode1
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature MatchingCode2
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views—0
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction—0
ObjectSDF++: Improved Object-Compositional Neural Implicit SurfacesCode1
FLAME-based Multi-View 3D Face Reconstruction—0
A One Stop 3D Target Reconstruction and multilevel Segmentation MethodCode1
Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping—0
HGDNet: A Height-Hierarchy Guided Dual-Decoder Network for Single View Building Extraction and Height Estimation—0
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape ProgramsCode1
Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement—0
Learning Photometric Feature Transform for Free-form Object Scan—0
Reconstructing Three-Dimensional Models of Interacting HumansCode1
Stereo Visual Odometry with Deep Learning-Based Point and Line Feature Matching using an Attention Graph Neural Network—0
Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body Reconstruction—0
Towards Head Computed Tomography Image Reconstruction Standardization with Deep Learning Assisted Automatic Detection—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—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