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 1301–1325 of 2326 papers

TitleStatusHype
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields—0
Neural Implicit Surface Reconstruction using Imaging Sonar—0
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes—0
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images using Images, Normals and Vertices—0
Articulated 3D Human-Object Interactions from RGB Videos: An Empirical Analysis of Approaches and ChallengesCode1
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions—0
3D Single-pixel imaging with active sampling patterns and learning based reconstruction—0
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings—0
A comprehensive survey on recent deep learning-based methods applied to surgical data—0
SimpleRecon: 3D Reconstruction Without 3D ConvolutionsCode3
Inferring Implicit 3D Representations from Human Figures on Pictorial Maps—0
Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative ImagingCode2
PeRFception: Perception using Radiance FieldsCode2
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth SamplingCode1
MD-Net: Multi-Detector for Local Feature Extraction—0
Visual-Inertial Multi-Instance Dynamic SLAM with Object-level RelocalisationCode1
PlaneFormers: From Sparse View Planes to 3D ReconstructionCode1
Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment—0
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based DepthCode2
Learning Object Manipulation Skills from Video via Approximate Differentiable PhysicsCode0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
Neural Correspondence Field for Object Pose Estimation—0
The One Where They Reconstructed 3D Humans and Environments in TV Shows—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