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/MichaelZhouwang/LMlexsub

Attention Is All You Need

https://github.com/Mind23-2/MindCode-109

PVT: Point-Voxel Transformer for Point Cloud Learning

https://github.com/Mind23-2/MindCode-5/tree/main/pointtransformer

Point Transformer

https://github.com/Mind23-2/MindCode-63

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

https://github.com/MindCode-4/code-5/tree/main/transformer

Attention Is All You Need

https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer

Point Transformer

https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

https://github.com/MindSpore-paper-code-3/code4/tree/main/frustum-pointnet

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

https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet

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

https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2

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

https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model

Attention Is All You Need

https://github.com/MindSpore-scientific/code-5/tree/main/sa-mlp-distilling-graph

SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained Environments

https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model

Attention Is All You Need

https://github.com/MirunaPislar/multi-head-attention-labeller

Attention Is All You Need

https://github.com/Misoknisky/Bert-MultiGpu

Attention Is All You Need

https://github.com/ModelBunker/PointNet-TensorFlow

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

https://github.com/MohamedAbdelsalam9/TT-Transformer

Attention Is All You Need

https://github.com/Moon-Xuezha/PointNet

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

https://github.com/NMADALI97/PointCNN_Pytorch

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

https://github.com/NVIDIA/sentiment-discovery

Attention Is All You Need

https://github.com/Neoanarika/torchexplainer

Attention Is All You Need

https://github.com/Nimesh-Patel/BERT

Attention Is All You Need

https://github.com/Nstats/bert_MRC

Attention Is All You Need

https://github.com/Nstats/bert_senti_analysis_ch

Attention Is All You Need

https://github.com/Nstats/my_bert

Attention Is All You Need

https://github.com/OPAYA/Transformer

Attention Is All You Need

https://github.com/OpenSpaceAI/OTMae3D

https://github.com/Osobarako/alduswarrensewell

Attention Is All You Need

https://github.com/POSTECH-CVLab/FastPointTransformer

Point Transformer

https://github.com/POSTECH-CVLab/point-transformer

Point Transformer

https://github.com/PaParaZz1/PointNet

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

https://github.com/PaddlePaddle/Paddle3D

PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer

Attention Is All You Need

https://github.com/PaddlePaddle/PaddleSpeech

Attention Is All You Need

https://github.com/Pezhin/best_yelp_review_project_ever

Attention Is All You Need

https://github.com/Phimos/Paddle-PointNet

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

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