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 676–700 of 2326 papers

TitleStatusHype
Coherent 3D Portrait Video Reconstruction via Triplane Fusion—0
NC-SDF: Enhancing Indoor Scene Reconstruction Using Neural SDFs with View-Dependent Normal Compensation—0
Depth Priors in Removal Neural Radiance Fields—0
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian Splatting—0
Lightplane: Highly-Scalable Components for Neural 3D FieldsCode3
MicroDreamer: Efficient 3D Generation in 20 Seconds by Score-based Iterative ReconstructionCode2
Reconstructing Satellites in 3D from Amateur Telescope Images—0
Bootstrap-GS: Self-Supervised Augmentation for High-Fidelity Gaussian Splatting—0
LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling 3D GaussiansCode0
PhyRecon: Physically Plausible Neural Scene Reconstruction—0
3D Freehand Ultrasound using Visual Inertial and Deep Inertial Odometry for Measuring Patellar Tracking—0
OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian SegmentationCode0
Neural Radiance Field in Autonomous Driving: A Survey—0
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
Advancing Applications of Satellite Photogrammetry: Novel Approaches for Built-up Area Modeling and Natural Environment Monitoring using Stereo/Multi-view Satellite Image-derived 3D Data—0
6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene ReconstructionCode2
A Subspace-Constrained Tyler's Estimator and its Applications to Structure from MotionCode0
RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans—0
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape ReconstructionCode1
CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders—0
Taming Latent Diffusion Model for Neural Radiance Field Inpainting—0
LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives—0
EGGS: Edge Guided Gaussian Splatting for Radiance Fields—0
Probabilistic Directed Distance Fields for Ray-Based Shape Representations—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