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 1801–1825 of 2326 papers

TitleStatusHype
Heightmap Reconstruction of Macula on Color Fundus Images Using Conditional Generative Adversarial NetworksCode0
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDARCode0
Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects—0
Generative Adversarial Networks for Robust Cryo-EM Image DenoisingCode0
Attention-based 3D Object Reconstruction from a Single Image—0
RocNet: Recursive Octree Network for Efficient 3D Deep Representation—0
Integration of the 3D Environment for UAV Onboard Visual Object Tracking—0
Eyeglasses 3D shape reconstruction from a single face image—0
Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation—0
GSIR: Generalizable 3D Shape Interpretation and Reconstruction—0
PL₁P - Point-line Minimal Problems under Partial Visibility in Three Views—0
FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras—0
Multi-Agent Embodied Question Answering in Interactive Environments—0
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry—0
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone—0
Polarimetric Multi-View Inverse Rendering—0
Co-Attention for Conditioned Image Matching—0
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving—0
PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network—0
Inferring the 3D Standing Spine Posture from 2D Radiographs—0
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation—0
Single-Frame based Deep View Synchronization for Unsynchronized Multi-Camera SurveillanceCode0
Multi view stereo with semantic priors—0
Task-agnostic Temporally Consistent Facial Video Editing—0
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography—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