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 751–775 of 2326 papers

TitleStatusHype
GarmentCrafter: Progressive Novel View Synthesis for Single-View 3D Garment Reconstruction and Editing—0
3D Reconstruction of non-visible surfaces of objects from a Single Depth View -- Comparative Study—0
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition—0
Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
CAT3D: Create Anything in 3D with Multi-View Diffusion Models—0
Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint assessment in shoulder CT scans—0
Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery—0
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot—0
PanicleNeRF: low-cost, high-precision in-field phenotypingof rice panicles with smartphone—0
Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis—0
Exploiting Object Similarity in 3D Reconstruction—0
Extreme Rotation Estimation in the Wild—0
Ex vivo experiment on vertebral body with defect representing bone metastasis—0
Eyeglasses 3D shape reconstruction from a single face image—0
CBARF: Cascaded Bundle-Adjusting Neural Radiance Fields from Imperfect Camera Poses—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—0
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction—0
Exploiting High Level Scene Cues in Stereo Reconstruction—0
Facetwise Mesh Refinement for Multi-View Stereo—0
Facial Surgery Preview Based on the Orthognathic Treatment Prediction—0
Fast 3D Reconstruction of Faces With Glasses—0
A Framework for the Volumetric Integration of Depth Images—0
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information—0
Exploiting 3D Shape Bias towards Robust Vision—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