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3D Shape Recognition
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Deep Learning Framework for Three Dimensional Shape Reconstruction from Phaseless Acoustic Scattering Far-field Data
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition
Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
ViewFormer: View Set Attention for Multi-view 3D Shape Understanding
MVTN: Learning Multi-View Transformations for 3D Understanding
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition
MVTN: Multi-View Transformation Network for 3D Shape Recognition
Invariant 3D Shape Recognition using Predictive Modular Neural Networks
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
MANet: Multimodal Attention Network based Point- View fusion for 3D Shape Recognition
On Learning Sets of Symmetric Elements
HRGE-Net: Hierarchical Relational Graph Embedding Network for Multi-view 3D Shape Recognition
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition
Multiview Based 3D Scene Understanding On Partial Point Sets
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
Learning Discriminative 3D Shape Representations by View Discerning Networks
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Multi-view Convolutional Neural Networks for 3D Shape Recognition
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.