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 776–800 of 2326 papers

TitleStatusHype
Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction—0
A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography—0
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes—0
3D GANs and Latent Space: A comprehensive survey—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos—0
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision—0
Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications—0
Fast Non-Rigid Radiance Fields from Monocularized Data—0
CharNeRF: 3D Character Generation from Concept Art—0
Fast Point Cloud to Mesh Reconstruction for Deformable Object Tracking—0
ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events—0
Exact Bias Correction and Covariance Estimation for Stereo Vision—0
FAWN: Floor-And-Walls Normal Regularization for Direct Neural TSDF Reconstruction—0
GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting—0
Feature Based Framework to Detect Diseases, Tumor, and Bleeding in Wireless Capsule Endoscopy—0
Feature Descriptors for Tracking by Detection: a Benchmark—0
EV-SegNet: Semantic Segmentation for Event-based Cameras—0
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene—0
EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping—0
EventNeRF: Neural Radiance Fields from a Single Colour Event Camera—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations—0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces—0
3D Reconstruction of Multiple Objects by mmWave Radar on UAV—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