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 1951–1975 of 2326 papers

TitleStatusHype
3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera—0
Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputs—0
3D Freehand Ultrasound using Visual Inertial and Deep Inertial Odometry for Measuring Patellar Tracking—0
Visual Enhanced 3D Point Cloud Reconstruction from A Single Image—0
SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians—0
SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction—0
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces—0
SiLK: Simple Learned Keypoints—0
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification—0
Visual enhancement and 3D representation for underwater scenes: a review—0
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface—0
Simultaneous 3D Reconstruction for Water Surface and Underwater Scene—0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications—0
Single-cell 3D genome reconstruction in the haploid setting using rigidity theory—0
Single-image coherent reconstruction of objects and humans—0
Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data—0
3D face reconstruction from single image with generative adversarial networks—0
Single-view 3D Body and Cloth Reconstruction under Complex Poses—0
Single-view 3D reconstruction via inverse procedural modeling—0
Single-View 3D Reconstruction via SO(2)-Equivariant Gaussian Sculpting Networks—0
Single-View 3D Scene Parsing by Attributed Grammar—0
Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture—0
X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph—0
Single View Refractive Index Tomography with Neural Fields—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