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 1901–1950 of 2326 papers

TitleStatusHype
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for RoboticsCode0
Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions—0
Leveraging Vision Reconstruction Pipelines for Satellite Imagery—0
An Interactive Control Approach to 3D Shape Reconstruction—0
Higher Order Function Networks for View Planning and Multi-View Reconstruction—0
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping—0
PLMP - Point-Line Minimal Problems in Complete Multi-View Visibility—0
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics—0
Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving—0
Unsupervised 3D Reconstruction Networks—0
X-Section: Cross-Section Prediction for Enhanced RGB-D Fusion—0
Track to Reconstruct and Reconstruct to TrackCode0
An Efficient Sampling-based Method for Online Informative Path Planning in Unknown EnvironmentsCode0
Weakly-Supervised 3D Pose Estimation from a Single Image using Multi-View Consistency—0
CvxNet: Learnable Convex Decomposition—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video—0
Few-Shot Generalization for Single-Image 3D Reconstruction via Priors—0
Self-Supervised Deep Depth DenoisingCode0
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence—0
Learned Semantic Multi-Sensor Depth Map Fusion—0
Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction—0
Multi-Spectral Visual Odometry without Explicit Stereo Matching—0
Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance from Motion—0
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single ImageCode0
DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data—0
Matching-based Depth Camera and Mirrors for 3D Reconstruction—0
Hyperparameter-Free Losses for Model-Based Monocular ReconstructionCode0
Directional TSDF: Modeling Surface Orientation for Coherent Meshes—0
Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing—0
3D Reconstruction of Deformable Revolving Object under Heavy Hand Interaction—0
Silhouette Guided Point Cloud Reconstruction beyond OcclusionCode0
PU-GAN: a Point Cloud Upsampling Adversarial NetworkCode0
Learning Embedding of 3D models with Quadric LossCode0
Pose-variant 3D Facial Attribute Generation—0
Temporally Consistent Horizon LinesCode0
Stereo-based terrain traversability analysis using normal-based segmentation and superpixel surface analysis—0
S&CNet: Monocular Depth Completion for Autonomous Systems and 3D Reconstruction—0
Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction—0
Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network—0
Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumesCode0
A linear method for camera pair self-calibration and multi-view reconstruction with geometrically verified correspondences—0
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction—0
3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging—0
3D Instance Segmentation via Multi-Task Metric Learning—0
Learning to Reconstruct and Understand Indoor Scenes from Sparse Views—0
Analytical Derivatives for Differentiable Renderer: 3D Pose Estimation by Silhouette Consistency—0
Differentiable probabilistic models of scientific imaging with the Fourier slice theoremCode0
Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era—0
Large-Scale, Metric Structure From Motion for Unordered Light Fields—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