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
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image—0
Analysis of Sampling Strategies for Implicit 3D Reconstruction—0
Gaussian Time Machine: A Real-Time Rendering Methodology for Time-Variant Appearances—0
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review—0
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction—0
Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning—0
3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motion—0
3D-HGS: 3D Half-Gaussian Splatting—0
3D Building Reconstruction From Monocular Remote Sensing Images—0
Gaussian Splashing: Direct Volumetric Rendering Underwater—0
Gaussian RBFNet: Gaussian Radial Basis Functions for Fast and Accurate Representation and Reconstruction of Neural Fields—0
Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry—0
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion—0
Gaussian Fusion: Accurate 3D Reconstruction via Geometry-Guided Displacement Interpolation—0
GaussianFocus: Constrained Attention Focus for 3D Gaussian Splatting—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion—0
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
Computer vision tasks for intelligent aerospace missions: An overview—0
GaussianCAD: Robust Self-Supervised CAD Reconstruction from Three Orthographic Views Using 3D Gaussian Splatting—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
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians—0
Computer vision in automated parking systems: Design, implementation and challenges—0
An adversarial feature learning based semantic communication method for Human 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