Papers with Code 아카이브

3D Point Cloud Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 4
평가표: 5
보존된 평가 기록: 216

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/KiranAkadas/My_Pointnet_v2

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/Kirankumaraswamy/Transformers

Attention Is All You Need

https://github.com/KrishnaTarun/Deep-Learning-Lab

Attention Is All You Need

https://github.com/Kyubyong/transformer

Attention Is All You Need

https://github.com/LONG-9621/3DCNN

Volumetric and Multi-View CNNs for Object Classification on 3D Data

https://github.com/LONG-9621/Extract_Point_3D

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/LONG-9621/PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/LONG-9621/PointNet-

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/LONG-9621/SO-Net

SO-Net: Self-Organizing Network for Point Cloud Analysis

https://github.com/LONG-9621/VoteNet

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/LebronGG/PointCnn

PointCNN: Convolution On $\mathcal{X}$-Transformed Points

https://github.com/LebronGG/PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/Lemoners/Attention-Is-All-You-Need

Attention Is All You Need

https://github.com/LeoWood/bert-horovod

Attention Is All You Need

https://github.com/LiamMaclean216/Pytorch-Chatbot

Attention Is All You Need

https://github.com/LiamMaclean216/Pytorch-Transfomer

Attention Is All You Need

https://github.com/LinZhuoChen/pointnet2_multi_gpu

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/Liu-Feng/PCT-tensorflow

PCT: Point cloud transformer

https://github.com/Lonepic/SPIB

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/LoveYang/bert_test

Attention Is All You Need

https://github.com/Lsdefine/attention-is-all-you-need-keras

Attention Is All You Need

https://github.com/Lw510107/PointCNN

PointCNN: Convolution On $\mathcal{X}$-Transformed Points

https://github.com/Lw510107/PointNet

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/Lw510107/pointnet-2018.6.27-

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/MCLYang/RhythmGAN_pytorch

Attention Is All You Need

https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted

Attention Is All You Need

https://github.com/MS-Mind/MS-Code-06/tree/main/pointtransformer

Point Transformer

https://github.com/MaZhiyuanBUAA/bert-tf1.4.0

Attention Is All You Need

https://github.com/Maple728/transformer

Attention Is All You Need

https://github.com/MarcBS/keras

Attention Is All You Need

https://github.com/MashaKubyshina/learning_ML

Attention Is All You Need

https://github.com/Matthewdowney18/Transformer_Dialogue

Attention Is All You Need

https://github.com/Maz101/Bert

Attention Is All You Need

https://github.com/Maz101/pretrainingdataset

Attention Is All You Need

https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/Meowuu7/Point-Transformer

Point Transformer

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.