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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1525 of 2326 papers

TitleStatusHype
SPIDR: SDF-based Neural Point Fields for Illumination and Deformation—0
DeepMLE: A Robust Deep Maximum Likelihood Estimator for Two-view Structure from Motion—0
Floorplan-Aware Camera Poses Refinement—0
3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit ModelsCode0
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review—0
MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors—0
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
WaterNeRF: Neural Radiance Fields for Underwater Scenes—0
3D Reconstruction using Structured Light from off-the-shelf components—0
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision—0
BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction—0
Joint Reconstruction and Parcellation of Cortical Surfaces—0
Neural Implicit Surface Reconstruction using Imaging Sonar—0
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields—0
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images using Images, Normals and Vertices—0
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes—0
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions—0
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings—0
3D Single-pixel imaging with active sampling patterns and learning based reconstruction—0
A comprehensive survey on recent deep learning-based methods applied to surgical data—0
Inferring Implicit 3D Representations from Human Figures on Pictorial Maps—0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
MD-Net: Multi-Detector for Local Feature Extraction—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