https://github.com/ajhamdi/MVTN
공식 코드MVTN: Multi-View Transformation Network for 3D Shape Recognition
Papers with Code 아카이브
3D Shape Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ajhamdi/mvtorch
공식 코드MVTN: Learning Multi-View Transformations for 3D Understanding
https://github.com/axeber01/point-tnt
공식 코드Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition
https://github.com/bigdata-graph/gmvit
공식 코드Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
https://github.com/juho-lee/set_transformer
공식 코드Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/keeganhk/pointmcd
공식 코드https://github.com/maria-korosteleva/Garment-Pattern-Estimation
공식 코드NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments
https://github.com/myavartanoo/PolyNet_PyTorch
공식 코드PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
https://github.com/weixmath/cvr
공식 코드Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
https://github.com/ChengXu1995/test
Learning Discriminative 3D Shape Representations by View Discerning Networks
https://github.com/DLii-Research/tf-set-transformer
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/Enigmatisms/Set-Transformer
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/Haggaim/On-Learning-Sets-of-Symmetric-Elements
https://github.com/NVlabs/On-Learning-Sets-of-Symmetric-Elements
https://github.com/Rishit-dagli/isab
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/Xianchao-Wu/perceiver-pytorch
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/chengz3906/View-Discerning-Network
Learning Discriminative 3D Shape Representations by View Discerning Networks
https://github.com/code-implementation1/Code9/tree/main/PVNet
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
https://github.com/lucidrains/agent-attention-pytorch
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/lucidrains/perceiver-pytorch
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/pientist/ballradar
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/rasoulnorouzi/Health-Recipes-Recommender/tree/main/Models/Transformers
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/sagieppel/testing-large-vision-language-models-lvlm-on-visual-questions
Shape and Texture Recognition in Large Vision-Language Models
https://github.com/xuxucmkox/3D-shape-Classification-Beam-Search
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.