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 551–575 of 2326 papers

TitleStatusHype
Doppelgangers: Learning to Disambiguate Images of Similar StructuresCode1
D2-Net: A Trainable CNN for Joint Detection and Description of Local FeaturesCode1
DUDF: Differentiable Unsigned Distance Fields with Hyperbolic ScalingCode1
E3D: Event-Based 3D Shape ReconstructionCode1
FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face ExtractionCode1
GP-GS: Gaussian Processes for Enhanced Gaussian SplattingCode1
GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View StereoCode1
IVS3D: An Open Source Framework for Intelligent Video Sampling and Preprocessing to Facilitate 3D ReconstructionCode1
MCN-SLAM: Multi-Agent Collaborative Neural SLAM with Hybrid Implicit Neural Scene RepresentationCode1
PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision ModelsCode1
3D Pose Estimation of Two Interacting Hands from a Monocular Event CameraCode1
Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
A Real World Dataset for Multi-view 3D Reconstruction—0
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings—0
3D Modeling: Camera Movement Estimation and path Correction for SFM Model using the Combination of Modified A-SIFT and Stereo System—0
Deep-Learning Assisted High-Resolution Binocular Stereo Depth Reconstruction—0
Deep Learned Full-3D Object Completion from Single View—0
A Real-Time Human Pose Measurement System for Human-In-The-Loop Dynamic Simulators—0
DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions—0
3D Colored Shape Reconstruction from a Single RGB Image through Diffusion—0
D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning—0
Deep Event Stereo Leveraged by Event-to-Image Translation—0
3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency—0
AR4D: Autoregressive 4D Generation from Monocular Videos—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