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 1051–1100 of 2326 papers

TitleStatusHype
Blind Face Restoration via Integrating Face Shape and Generative Priors—0
Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping—0
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials—0
ActiveRMAP: Radiance Field for Active Mapping And Planning—0
360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images—0
Efficient Image-Space Extraction and Representation of 3D Surface Topography—0
Efficient Global Optimization of Non-differentiable, Symmetric Objectives for Multi Camera Placement—0
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation—0
Black-Box Test-Time Shape REFINEment for Single View 3D Reconstruction—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Efficient 3D Room Shape Recovery From a Single Panorama—0
Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments—0
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting—0
Edge Projection-Based Adaptive View Selection for Cone-Beam CT—0
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model—0
3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning—0
2L3: Lifting Imperfect Generated 2D Images into Accurate 3D—0
Portrait4D: Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data—0
Edge-preserving Near-light Photometric Stereo with Neural Surfaces—0
Edge Preserving Implicit Surface Representation of Point Clouds—0
Bi-objective Framework for Sensor Fusion in RGB-D Multi-View Systems: Applications in Calibration—0
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction—0
EasyVis2: A Real Time Multi-view 3D Visualization for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose—0
Binomial Self-Compensation: Mechanism and Suppression of Motion Error in Phase-Shifting Profilometry—0
Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation—0
A Cranial-Feature-Based Registration Scheme for Robotic Micromanipulation Using a Microscopic Stereo Camera System—0
Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages—0
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images—0
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface—0
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues—0
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering—0
3D Reconstruction from Sketches—0
Dynamic NeRF: A Review—0
BG-Triangle: Bézier Gaussian Triangle for 3D Vectorization and Rendering—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
DUST: Dual Union of Spatio-Temporal Subspaces for Monocular Multiple Object 3D Reconstruction—0
Beyond Existance: Fulfill 3D Reconstructed Scenes with Pseudo Details—0
Acoustic Neural 3D Reconstruction Under Pose Drift—0
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces—0
DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data—0
Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction—0
Betsu-Betsu: Multi-View Separable 3D Reconstruction of Two Interacting Objects—0
A Correspondence Relaxation Approach for 3D Shape Reconstruction—0
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction—0
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models—0
A Constrained Optimization Approach for Gaussian Splatting from Coarsely-posed Images and Noisy Lidar Point Clouds—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