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 2051–2100 of 2326 papers

TitleStatusHype
Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data—0
Experimental Results of a 3D Millimeter-Wave Compressive-Reflector-Antenna Imaging System—0
From handcrafted to deep local featuresCode0
Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision—0
The Double Sphere Camera ModelCode0
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single ImageCode0
GeoDesc: Learning Local Descriptors by Integrating Geometry ConstraintsCode0
Specular-to-Diffuse Translation for Multi-View Reconstruction—0
Combining SLAM with muti-spectral photometric stereo for real-time dense 3D reconstruction—0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry—0
Road surface 3d reconstruction based on dense subpixel disparity map estimationCode0
Optimal mechanical operation in the vicinity of curved vasculature—0
G2D: from GTA to DataCode0
IVUS-Net: An Intravascular Ultrasound Segmentation NetworkCode0
Soccer on Your Tabletop—0
Trapping Light for Time of Flight—0
SobolevFusion: 3D Reconstruction of Scenes Undergoing Free Non-Rigid Motion—0
Robust Hough Transform Based 3D Reconstruction From Circular Light Fields—0
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories—0
Texture Mapping for 3D Reconstruction With RGB-D Sensor—0
Augmenting Crowd-Sourced 3D Reconstructions Using Semantic Detections—0
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of VehiclesCode0
GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D Reconstruction—0
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video—0
Predicting the Next Best View for 3D Mesh RefinementCode0
Street-view change detection with deconvolutional networks—0
Full 3D Reconstruction of Transparent Objects—0
Adaptive View Planning for Aerial 3D Reconstruction—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Accurate Deep Direct Geo-Localization from Ground Imagery and Phone-Grade GPS—0
Liveness Detection Using Implicit 3D Features—0
Im2Avatar: Colorful 3D Reconstruction from a Single ImageCode0
Pix3D: Dataset and Methods for Single-Image 3D Shape ModelingCode0
Nonlinear 3D Face Morphable ModelCode0
Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter—0
3D Fluid Flow Estimation with Integrated Particle ReconstructionCode0
Learning Free-Form Deformations for 3D Object ReconstructionCode0
HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model—0
Learning to Reconstruct Texture-less Deformable Surfaces from a Single ViewCode0
Prioritized Multi-View Stereo Depth Map Generation Using Confidence Prediction—0
Aligning Across Large Gaps in Time—0
Progressive Structure from Motion—0
Consistent sets of lines with no colorful incidence—0
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild—0
Video Based Reconstruction of 3D People ModelsCode0
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey—0
A Neural Network Approach to Missing Marker Reconstruction in Human Motion CaptureCode0
Methodology to analyze the accuracy of 3D objects reconstructed with collaborative robot based monocular LSD-SLAM—0
Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery—0
Multi-Sensor Integration for Indoor 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