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 1826–1850 of 2326 papers

TitleStatusHype
A simulation environment for drone cinematography—0
SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera Motion—0
A Solution for Crime Scene Reconstruction using Time-of-Flight Cameras—0
A Spatial-temporal 3D Human Pose Reconstruction Framework—0
A Survey of 3D Reconstruction with Event Cameras: From Event-based Geometry to Neural 3D Rendering—0
Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning—0
A Survey on Event-driven 3D Reconstruction: Development under Different Categories—0
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
A System for 3D Reconstruction Of Comminuted Tibial Plafond Bone Fractures—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
A Theory of Refractive Photo-Light-Path Triangulation—0
A Theory of Topological Derivatives for Inverse Rendering of Geometry—0
Objects as volumes: A stochastic geometry view of opaque solids—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
Spike-NeRF: Neural Radiance Field Based On Spike Camera—0
Attention-based 3D Object Reconstruction from a Single Image—0
Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts—0
Splat and Replace: 3D Reconstruction with Repetitive Elements—0
SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable Surface—0
Augmenting Crowd-Sourced 3D Reconstructions Using Semantic Detections—0
AURORA: Automated Unleash of 3D Room Outlines for VR Applications—0
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation—0
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape—0
Automated 3D Face Reconstruction From Multiple Images Using Quality Measures—0
Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands—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