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 501–525 of 2326 papers

TitleStatusHype
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning ApplicationsCode0
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion ModelsCode3
Single-View 3D Reconstruction via SO(2)-Equivariant Gaussian Sculpting Networks—0
Sources of Uncertainty in 3D Scene ReconstructionCode1
Online 3D reconstruction and dense tracking in endoscopic videosCode1
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering—0
Ultron: Enabling Temporal Geometry Compression of 3D Mesh Sequences using Temporal Correspondence and Mesh DeformationCode0
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes—0
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction—0
LM-Gaussian: Boost Sparse-view 3D Gaussian Splatting with Large Model Priors—0
Automatic occlusion removal from 3D maps for maritime situational awareness—0
Object Gaussian for Monocular 6D Pose Estimation from Sparse Views—0
Coral Model Generation from Single Images for Virtual Reality Applications—0
MetaFood3D: 3D Food Dataset with Nutrition Values—0
EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular VideoCode3
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View SynthesisCode5
Free-DyGS: Camera-Pose-Free Scene Reconstruction based on Gaussian Splatting for Dynamic Surgical Videos—0
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation ModelCode1
GMM-IKRS: Gaussian Mixture Models for Interpretable Keypoint Refinement and Scoring—0
2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Reconstruction—0
Mismatched: Evaluating the Limits of Image Matching Approaches and BenchmarksCode1
Spurfies: Sparse Surface Reconstruction using Local Geometry Priors—0
3D Reconstruction with Spatial MemoryCode5
GeoTransfer : Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning—0
Learning-based Multi-View Stereo: A Survey—0
Show:102550
← PrevPage 21 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