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 1126–1150 of 2326 papers

TitleStatusHype
CD^2: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance—0
Facetwise Mesh Refinement for Multi-View Stereo—0
CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting—0
Face Normals "In-The-Wild" Using Fully Convolutional Networks—0
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction—0
A Generative Approach to High Fidelity 3D Reconstruction from Text Data—0
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction—0
3D Reconstruction of Protein Complex Structures Using Synthesized Multi-View AFM Images—0
3D Annotation Of Arbitrary Objects In The Wild—0
Efficient NeRF Optimization -- Not All Samples Remain Equally Hard—0
UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization—0
Eyeglasses 3D shape reconstruction from a single face image—0
CBARF: Cascaded Bundle-Adjusting Neural Radiance Fields from Imperfect Camera Poses—0
Ex vivo experiment on vertebral body with defect representing bone metastasis—0
Extreme Rotation Estimation in the Wild—0
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
CAT3D: Create Anything in 3D with Multi-View Diffusion Models—0
Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery—0
Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint assessment in shoulder CT scans—0
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot—0
3D Reconstruction of non-visible surfaces of objects from a Single Depth View -- Comparative Study—0
Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis—0
Exploiting Object Similarity in 3D Reconstruction—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—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