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 1451–1475 of 2326 papers

TitleStatusHype
PL_1P -- Point-line Minimal Problems under Partial Visibility in Three Views—0
PL₁P - Point-line Minimal Problems under Partial Visibility in Three Views—0
Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes—0
What Do Single-view 3D Reconstruction Networks Learn?—0
Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone—0
Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image—0
Unsupervised single-particle deep clustering via statistical manifold learning—0
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar—0
PLMP - Point-Line Minimal Problems in Complete Multi-View Visibility—0
Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction—0
3DPX: Single Panoramic X-ray Analysis Guided by 3D Oral Structure Reconstruction—0
3D photogrammetry point cloud segmentation using a model ensembling framework—0
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
PointRecon: Online Point-based 3D Reconstruction via Ray-based 2D-3D Matching—0
Polarimetric Helmholtz Stereopsis—0
Polarimetric Multi-View Inverse Rendering—0
Polarimetric Multi-View Inverse Rendering—0
Polarimetric Multi-View Stereo—0
Polarized 3D: High-Quality Depth Sensing With Polarization Cues—0
3D-NVS: A 3D Supervision Approach for Next View Selection—0
PO-MSCKF: An Efficient Visual-Inertial Odometry by Reconstructing the Multi-State Constrained Kalman Filter with the Pose-only Theory—0
Ponder: Point Cloud Pre-training via Neural Rendering—0
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data—0
Pooling Image Datasets With Multiple Covariate Shift and Imbalance—0
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian—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