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 2001–2025 of 2326 papers

TitleStatusHype
CSS: Overcoming Pose and Scene Challenges in Crowd-Sourced 3D Gaussian Splatting—0
Ctrl123: Consistent Novel View Synthesis via Closed-Loop Transcription—0
Structure From Motion Using Structure-Less Resection—0
Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison—0
CvxNet: Learnable Convex Decomposition—0
Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform—0
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle—0
D2D: Keypoint Extraction with Describe to Detect Approach—0
Co-Attention for Conditioned Image Matching—0
D^2IM-Net: Learning Detail Disentangled Implicit Fields from Single Images—0
D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images—0
D^2USt3R: Enhancing 3D Reconstruction with 4D Pointmaps for Dynamic Scenes—0
Aligning Across Large Gaps in Time—0
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization—0
DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial Basis Functions—0
DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography—0
Structure-from-Sherds++: Robust Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections—0
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model—0
Style3D: Attention-guided Multi-view Style Transfer for 3D Object Generation—0
Style Agnostic 3D Reconstruction via Adversarial Style Transfer—0
Sub-Image Recapture for Multi-View 3D Reconstruction—0
A Two-step Calibration Method for Unfocused Light Field Camera Based on Projection Model Analysis—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction—0
Deep Compressed Learning for 3D Seismic Inversion—0
Show:102550
← PrevPage 81 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