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 851–875 of 2326 papers

TitleStatusHype
EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction—0
GaPT-DAR: Category-level Garments Pose Tracking via Integrated 2D Deformation and 3D Reconstruction—0
GarmentCrafter: Progressive Novel View Synthesis for Single-View 3D Garment Reconstruction and Editing—0
Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study—0
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians—0
GaussianCAD: Robust Self-Supervised CAD Reconstruction from Three Orthographic Views Using 3D Gaussian Splatting—0
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation—0
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation—0
Gaussian Fusion: Accurate 3D Reconstruction via Geometry-Guided Displacement Interpolation—0
Gaussian RBFNet: Gaussian Radial Basis Functions for Fast and Accurate Representation and Reconstruction of Neural Fields—0
Gaussian Splashing: Direct Volumetric Rendering Underwater—0
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review—0
Evaluation of Flight Parameters in UAV-based 3D Reconstruction for Rooftop Infrastructure Assessment—0
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild—0
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention—0
GauU-Scene: A Scene Reconstruction Benchmark on Large Scale 3D Reconstruction Dataset Using Gaussian Splatting—0
Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method—0
Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions—0
GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger—0
CAD Priors for Accurate and Flexible Instance Reconstruction—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
3D Reconstruction of Interacting Multi-Person in Clothing from a Single Image—0
Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting—0
Evaluating geometric accuracy of NeRF reconstructions compared to SLAM method—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