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 2076–2100 of 2326 papers

TitleStatusHype
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue—0
Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture—0
Dense Keypoints via Multiview Supervision—0
Dense Matchers for Dense Tracking—0
Dense Multi-view 3D-reconstruction Without Dense Correspondences—0
Dense Reconstruction Using 3D Object Shape Priors—0
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension—0
Dense RGB-D-Inertial SLAM with Map Deformations—0
Dense RGB-D semantic mapping with Pixel-Voxel neural network—0
Dense-SfM: Structure from Motion with Dense Consistent Matching—0
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video—0
Dense Voxel 3D Reconstruction Using a Monocular Event Camera—0
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience—0
Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth Priors in Removal Neural Radiance Fields—0
DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments—0
Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction—0
DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering—0
Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction—0
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling—0
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction—0
DG-Recon: Depth-Guided Neural 3D Scene Reconstruction—0
Dietary Intake Estimation via Continuous 3D Reconstruction of Food—0
Show:102550
← PrevPage 84 of 94Next →
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