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3D Shape Recognition

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Papers

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Showing 1–25 of 35 papers

TitleStatusHype
View-GCN: View-Based Graph Convolutional Network for 3D Shape AnalysisCode1
MVTN: Learning Multi-View Transformations for 3D UnderstandingCode1
MVTN: Multi-View Transformation Network for 3D Shape RecognitionCode1
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of GarmentsCode1
On Learning Sets of Symmetric ElementsCode1
Points to Patches: Enabling the Use of Self-Attention for 3D Shape RecognitionCode1
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape RepresentationCode1
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary ViewsCode1
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksCode1
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape RecognitionCode1
Group Multi-View Transformer for 3D Shape Analysis with Spatial EncodingCode0
Learning Discriminative 3D Shape Representations by View Discerning NetworksCode0
Beam Search for Learning a Deep Convolutional Neural Network of 3D ShapesCode0
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape RecognitionCode0
Shape and Texture Recognition in Large Vision-Language ModelsCode0
MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition—0
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition—0
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition—0
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences—0
ViewFormer: View Set Attention for Multi-view 3D Shape Understanding—0
Multiview Based 3D Scene Understanding On Partial Point Sets—0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition—0
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes—0
LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
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