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 2151–2175 of 2326 papers

TitleStatusHype
DreamTexture: Shape from Virtual Texture with Analysis by Augmentation—0
Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics—0
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction—0
Drone-based AI and 3D Reconstruction for Digital Twin Augmentation—0
DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing Neural Radiance Fields—0
DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery—0
Drought Stress Classification using 3D Plant Models—0
DSG-World: Learning a 3D Gaussian World Model from Dual State Videos—0
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models—0
DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Survey on Fundamental Deep Learning 3D Reconstruction Techniques—0
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction—0
DUST: Dual Union of Spatio-Temporal Subspaces for Monocular Multiple Object 3D Reconstruction—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
Dynamic NeRF: A Review—0
Survey on Monocular Metric Depth Estimation—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid—0
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications—0
Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation—0
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition—0
EasyVis2: A Real Time Multi-view 3D Visualization for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose—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