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 1426–1450 of 2326 papers

TitleStatusHype
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction—0
X-SG^2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks—0
Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction—0
Photometric Correction for Infrared Sensors—0
PanicleNeRF: low-cost, high-precision in-field phenotypingof rice panicles with smartphone—0
Photometric Stereo in a Scattering Medium—0
Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter—0
Enhanced 3D Urban Scene Reconstruction and Point Cloud Densification using Gaussian Splatting and Google Earth Imagery—0
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing—0
PHRIT: Parametric Hand Representation with Implicit Template—0
Unposed Sparse Views Room Layout Reconstruction in the Age of Pretrain Model—0
PhyRecon: Physically Plausible Neural Scene Reconstruction—0
Physically Compatible 3D Object Modeling from a Single Image—0
Physically Plausible 3D Human-Scene Reconstruction from Monocular RGB Image using an Adversarial Learning Approach—0
Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning—0
Physics-informed neural networks for gravity currents reconstruction from limited data—0
Piecewise-Planar 3D Approximation From Wide-Baseline Stereo—0
PiMPeR: Piecewise Dense 3D Reconstruction from Multi-View and Multi-Illumination Images—0
Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras—0
Unsupervised 3D Reconstruction Networks—0
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction—0
Pixel-Aligned Multi-View Generation with Depth Guided Decoder—0
3D Reconstruction and Alignment by Consumer RGB-D Sensors and Fiducial Planar Markers for Patient Positioning in Radiation Therapy—0
3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views—0
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion—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