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 1476–1500 of 2326 papers

TitleStatusHype
3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue—0
3D Reconstruction of Protein Complex Structures Using Synthesized Multi-View AFM Images—0
Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image—0
Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments—0
ActiveRMAP: Radiance Field for Active Mapping And Planning—0
Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques—0
SegNeRF: 3D Part Segmentation with Neural Radiance Fields—0
Efficient Second-Order Plane Adjustment—0
IC3D: Image-Conditioned 3D Diffusion for Shape Generation—0
Semantic keypoint extraction for scanned animals using multi-depth-camera systemsCode0
Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion—0
3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection—0
Batch-based Model Registration for Fast 3D Sherd Reconstruction—0
HOReeNet: 3D-aware Hand-Object Grasping Reenactment—0
Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories—0
GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance TradeoffCode0
3D Reconstruction of Multiple Objects by mmWave Radar on UAV—0
Physics-informed neural networks for gravity currents reconstruction from limited data—0
Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single ImageCode0
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction—0
Learning Neural Radiance Fields from Multi-View Geometry—0
3D Reconstruction of unstained cells from a single defocused hologram—0
One-Shot Neural Fields for 3D Object Understanding—0
Pruning-based Topology Refinement of 3D Mesh using a 2D Alpha Mask—0
PCGen: Point Cloud Generator for LiDAR Simulation—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