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 1151–1175 of 2326 papers

TitleStatusHype
HGSLoc: 3DGS-based Heuristic Camera Pose Refinement—0
A comparative study of breast surface reconstruction for aesthetic outcome assessment—0
Weakly Supervised 3D Object Detection with Multi-Stage Generalization—0
Hieroglyph: Hierarchical Glia Graph Skeletonization and Matching—0
High-Accuracy Facial Depth Models derived from 3D Synthetic Data—0
Higher Order Function Networks for View Planning and Multi-View Reconstruction—0
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field—0
High Fidelity 3D Reconstructions with Limited Physical Views—0
A Comparative Neural Radiance Field (NeRF) 3D Analysis of Camera Poses from HoloLens Trajectories and Structure from Motion—0
High Quality Photometric Reconstruction using a Depth Camera—0
High-quality Point Cloud Oriented Normal Estimation via Hybrid Angular and Euclidean Distance Encoding—0
High Quality Structure From Small Motion for Rolling Shutter Cameras—0
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach—0
HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation—0
HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation—0
2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Reconstruction—0
The One Where They Reconstructed 3D Humans and Environments in TV Shows—0
Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement—0
Hologram: Realtime Holographic Overlays via LiDAR Augmented Reconstruction—0
HoloPOCUS: Portable Mixed-Reality 3D Ultrasound Tracking, Reconstruction and Overlay—0
HoloPose: Holistic 3D Human Reconstruction In-The-Wild—0
HOReeNet: 3D-aware Hand-Object Grasping Reenactment—0
The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods—0
The Photometry of Intrinsic Images—0
HRGS: Hierarchical Gaussian Splatting for Memory-Efficient High-Resolution 3D Reconstruction—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