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 1501–1525 of 2326 papers

TitleStatusHype
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in VideosCode1
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
Neural Fields as Learnable Kernels for 3D Reconstruction—0
Gradient-SDF: A Semi-Implicit Surface Representation for 3D ReconstructionCode1
VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance FieldCode1
Monocular Road Planar Parallax Estimation—0
Neural Fields in Visual Computing and BeyondCode2
ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction—0
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One—0
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view StereoCode1
Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras—0
Towards Panoptic 3D Parsing for Single Image in the Wild—0
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face ReconstructionCode1
Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements—0
A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms—0
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D ModelCode1
TMBuD: A dataset for urban scene building detectionCode0
Robust Multi-view Registration of Point Sets with Laplacian Mixture Model—0
High Fidelity 3D Reconstructions with Limited Physical Views—0
AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit RepresentationsCode1
IVS3D: An Open Source Framework for Intelligent Video Sampling and Preprocessing to Facilitate 3D ReconstructionCode1
Style Agnostic 3D Reconstruction via Adversarial Style Transfer—0
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric MappingCode1
3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with TransformersCode1
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—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