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 1651–1700 of 2326 papers

TitleStatusHype
SMPLicit: Topology-aware Generative Model for Clothed PeopleCode1
Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data—0
An Effective Loss Function for Generating 3D Models from Single 2D Image without RenderingCode1
S^3: Learnable Sparse Signal Superdensity for Guided Depth Estimation—0
A comprehensive survey on point cloud registration—0
A System for 3D Reconstruction Of Comminuted Tibial Plafond Bone Fractures—0
Rethinking Content and Style: Exploring Bias for Unsupervised DisentanglementCode1
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variableCode1
Deep Event Stereo Leveraged by Event-to-Image Translation—0
Consistent Recurrent Neural Networks for 3D Neuron Segmentation—0
A metric for evaluating 3D reconstruction and mapping performance with no ground truthingCode0
VIO-Aided Structure from Motion Under Challenging Environments—0
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging ApproachCode1
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene GraphsCode2
Stereo camera system calibration: the need of two sets of parameters—0
Weakly-Supervised Multi-Face 3D ReconstructionCode1
Learning Accurate Dense Correspondences and When to Trust ThemCode1
In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces—0
Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo—0
Polarimetric Helmholtz Stereopsis—0
3D Building Reconstruction From Monocular Remote Sensing Images—0
Adaptive Surface Reconstruction With Multiscale Convolutional Kernels—0
3DeepCT: Learning Volumetric Scattering Tomography of Clouds—0
Multi-View 3D Reconstruction With Transformers—0
Gaussian Fusion: Accurate 3D Reconstruction via Geometry-Guided Displacement Interpolation—0
Uncertainty-Aware Human Mesh Recovery From Video by Learning Part-Based 3D Dynamics—0
EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View StereoCode0
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise _p Norm—0
Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning—0
Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo—0
SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstructionCode1
From Points to Multi-Object 3D Reconstruction—0
Towards Automatic Digital Documentation and Progress Reporting of Mechanical Construction Pipes using Smartphones—0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation—0
Learning Complex 3D Human Self-Contact—0
Reconstructing Hand-Object Interactions in the Wild—0
Human Mesh Recovery from Multiple ShotsCode1
Learning Continuous Image Representation with Local Implicit Image FunctionCode2
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal EstimationCode1
D^2IM-Net: Learning Detail Disentangled Implicit Fields from Single Images—0
3D Scattering Tomography by Deep Learning with Architecture Tailored to Cloud Fields—0
DI-Fusion: Online Implicit 3D Reconstruction with Deep PriorsCode1
E3D: Event-Based 3D Shape ReconstructionCode1
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Online Adaptation for Consistent Mesh Reconstruction in the Wild—0
A grid-point detection method based on U-net for a structured light system—0
Automated Calibration of Mobile Cameras for 3D Reconstruction of Mechanical Pipes—0
Isometric Multi-Shape Matching—0
pixelNeRF: Neural Radiance Fields from One or Few ImagesCode2
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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