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 976–1000 of 2326 papers

TitleStatusHype
Building Proteins in a Day: Efficient 3D Molecular Reconstruction—0
Enhancing Face Recognition With Self-Supervised 3D Reconstruction—0
BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces—0
3D Reconstruction of Deformable Revolving Object under Heavy Hand Interaction—0
Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models—0
Enhancement of 3D Gaussian Splatting using Raw Mesh for Photorealistic Recreation of Architectures—0
BU-CVKit: Extendable Computer Vision Framework for Species Independent Tracking and Analysis—0
Enhanced Stable View Synthesis—0
AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards—0
Enhanced Multi-View Pedestrian Detection Using Probabilistic Occupancy Volume—0
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion—0
Adjustable Method Based on Body Parts for Improving the Accuracy of 3D Reconstruction in Visually Important Body Parts from Silhouettes—0
Inferring the 3D Standing Spine Posture from 2D Radiographs—0
Enhanced fringe-to-phase framework using deep learning—0
Enforcing 3D Topological Constraints in Composite Objects via Implicit Functions—0
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
Hyperspectral 3D Mapping of Underwater Environments—0
Energy Based Multi-model Fitting & Matching for 3D Reconstruction—0
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions—0
IC3D: Image-Conditioned 3D Diffusion for Shape Generation—0
BRUM: Robust 3D Vehicle Reconstruction from 360 Sparse Images—0
IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations—0
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance 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