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 2076–2100 of 2326 papers

TitleStatusHype
Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning—0
A Survey on Event-driven 3D Reconstruction: Development under Different Categories—0
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
A System for 3D Reconstruction Of Comminuted Tibial Plafond Bone Fractures—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
A Theory of Refractive Photo-Light-Path Triangulation—0
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
Incremental Multiview Point Cloud RegistrationCode0
OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian SegmentationCode0
ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy FieldsCode0
Sparse 3D Reconstruction via Object-Centric Ray SamplingCode0
Multiview Supervision By RegistrationCode0
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for RoboticsCode0
Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance FieldsCode0
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep LearningCode0
Mutli-View 3D Reconstruction using Knowledge DistillationCode0
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop ClosureCode0
Self-Supervised Deep Depth DenoisingCode0
One Framework to Register Them All: PointNet Encoding for Point Cloud AlignmentCode0
Differentiable probabilistic models of scientific imaging with the Fourier slice theoremCode0
LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and ScenesCode0
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D SupervisionCode0
PLMP -- Point-Line Minimal Problems in Complete Multi-View VisibilityCode0
3D Shape Reconstruction from Sketches via Multi-view Convolutional NetworksCode0
Single-Frame based Deep View Synchronization for Unsynchronized Multi-Camera SurveillanceCode0
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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