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 1126–1150 of 2326 papers

TitleStatusHype
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features—0
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction—0
LiM-Loc: Visual Localization with Dense and Accurate 3D Reference Maps Directly Corresponding 2D Keypoints to 3D LiDAR Point Clouds—0
A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms—0
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis—0
3D face reconstruction from single image with generative adversarial networks—0
Linea alba 3D morphometric variability by CT scan exploration—0
Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data—0
Domain Adaptation for Real-World Single View 3D Reconstruction—0
AutoRecon: Automated 3D Object Discovery and Reconstruction—0
A comparative study of breast surface reconstruction for aesthetic outcome assessment—0
3D Reconstruction by Looking: Instantaneous Blind Spot Detector for Indoor SLAM through Mixed Reality—0
D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning—0
AutoPartGen: Autogressive 3D Part Generation and Discovery—0
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes—0
Autonomous 3D Reconstruction Using a MAV—0
A Comparative Neural Radiance Field (NeRF) 3D Analysis of Camera Poses from HoloLens Trajectories and Structure from Motion—0
Enhanced Knee Kinematics: Leveraging Deep Learning and Morphing Algorithms for 3D Implant Modeling—0
Line-Sweep: Cross-Ratio For Wide-Baseline Matching and 3D Reconstruction—0
DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularization—0
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery—0
Distributed Bundle Adjustment—0
Distillation of Diffusion Features for Semantic Correspondence—0
Automatic occlusion removal from 3D maps for maritime situational awareness—0
Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement—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