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 426–450 of 2326 papers

TitleStatusHype
PointRecon: Online Point-based 3D Reconstruction via Ray-based 2D-3D Matching—0
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference—0
PF3plat: Pose-Free Feed-Forward 3D Gaussian SplattingCode3
Guide3D: A Bi-planar X-ray Dataset for 3D Shape Reconstruction—0
SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset—0
ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian SplattingsCode2
Neural rendering enables dynamic tomography—0
SCube: Instant Large-Scale Scene Reconstruction using VoxSplats—0
Neural Fields in Robotics: A SurveyCode5
Tracking and triangulating firefly flashes in field recordingsCode0
Large Spatial Model: End-to-end Unposed Images to Semantic 3DCode3
A Cranial-Feature-Based Registration Scheme for Robotic Micromanipulation Using a Microscopic Stereo Camera System—0
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes—0
E-3DGS: Gaussian Splatting with Exposure and Motion EventsCode1
EndoMetric: Near-Light Monocular Metric Scale Estimation in Endoscopy—0
UniGS: Modeling Unitary 3D Gaussians for Novel View Synthesis from Sparse-view ImagesCode1
Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint assessment in shoulder CT scans—0
Robotic Arm Platform for Multi-View Image Acquisition and 3D Reconstruction in Minimally Invasive Surgery—0
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
Cultural Heritage 3D Reconstruction with Diffusion NetworksCode0
3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications—0
Improving 3D Finger Traits Recognition via Generalizable Neural Rendering—0
Neurally Integrated Finite Elements for Differentiable Elasticity on Evolving Domains—0
SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction—0
Ego3DT: Tracking Every 3D Object in Ego-centric 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