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 1026–1050 of 2326 papers

TitleStatusHype
FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction—0
Complex Non-Rigid Motion 3D Reconstruction by Union of Subspaces—0
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable—0
Full 3D Reconstruction of Transparent Objects—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
From Single Image Query to Detailed 3D Reconstruction—0
Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and extraction of individual tree parameters—0
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction—0
3D Reconstruction of the Human Colon from Capsule Endoscope Video—0
From Points to Multi-Object 3D Reconstruction—0
From Point Clouds to Mesh Using Regression—0
Comparative Analysis of Advanced Feature Matching Algorithms in Challenging High Spatial Resolution Optical Satellite Stereo Scenarios—0
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction—0
From Low Field to High Value: Robust Cortical Mapping from Low-Field MRI—0
Compact Model Representation for 3D Reconstruction—0
Alligat0R: Pre-Training Through Co-Visibility Segmentation for Relative Camera Pose Regression—0
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation—0
From a few Accurate 2D Correspondences to 3D Point Clouds—0
Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories—0
A linear method for camera pair self-calibration and multi-view reconstruction with geometrically verified correspondences—0
3D Reconstruction of Temples in the Special Region of Yogyakarta By Using Close-Range Photogrammetry—0
3DGS^2: Near Second-order Converging 3D Gaussian Splatting—0
3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging—0
FroDO: From Detections to 3D Objects—0
FroDO: From Detections to 3D Objects—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