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 926–950 of 2326 papers

TitleStatusHype
Automatic extraction and 3D reconstruction of split wire from point cloud data based on improved DPC algorithm—0
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image—0
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field—0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis—0
Fast Point Cloud to Mesh Reconstruction for Deformable Object Tracking—0
IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D ReconstructionCode0
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation—0
Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field ImagesCode0
Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture—0
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
Deep Compressed Learning for 3D Seismic Inversion—0
Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery—0
GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View StereoCode1
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets—0
Open-NeRF: Towards Open Vocabulary NeRF Decomposition—0
Interaction-Driven Active 3D Reconstruction with Object Interiors—0
A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video—0
UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene—0
Single-view 3D reconstruction via inverse procedural modeling—0
ShapeGraFormer: GraFormer-Based Network for Hand-Object Reconstruction from a Single Depth Map—0
Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors—0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External TrackerCode1
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior—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