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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1176–1200 of 2326 papers

TitleStatusHype
Towards Unified 3D Hair Reconstruction from Single-View Portraits—0
Mobile Image Analysis Application for Mantoux Skin Test—0
MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks—0
Modelling Errors in X-ray Fluoroscopic Imaging Systems Using Photogrammetric Bundle Adjustment With a Data-Driven Self-Calibration Approach—0
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction—0
3D Reconstruction with Generalizable Neural Fields using Scene Priors—0
Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction—0
Monocular 3D Fingerprint Reconstruction and Unwarping—0
Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements—0
Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames—0
Monocular Road Planar Parallax Estimation—0
Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity—0
MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction—0
MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors—0
Towards Unified Structured Light Optimization—0
3D Reconstruction with Fast Dipole Sums—0
MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views—0
T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image—0
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video—0
MoReMouse: Monocular Reconstruction of Laboratory Mouse—0
Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement—0
Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation—0
MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models—0
MRI Breast tissue segmentation using nnU-Net for biomechanical modeling—0
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction—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