https://github.com/hansen7/OcCo
공식 코드Unsupervised Point Cloud Pre-Training via Occlusion Completion
Papers with Code 아카이브
3D Point Cloud Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/haotian-liu/maskpoint
공식 코드Masked Discrimination for Self-Supervised Learning on Point Clouds
https://github.com/hkust-vgd/scanobjectnn
공식 코드https://github.com/hkust-vgd/shellnet
공식 코드ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
https://github.com/hlei-ziyan/SPH3D-GCN
공식 코드Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
https://github.com/hrzhou2/adaptconv-master
공식 코드https://github.com/ignf/myria3d
공식 코드https://github.com/ignf/pacasam
공식 코드https://github.com/itailang/SampleNet
공식 코드https://github.com/jerryfeng2003/pointgst
공식 코드Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
https://github.com/jiachens/ModelNet40-C
공식 코드PointCutMix: Regularization Strategy for Point Cloud Classification
https://github.com/jiamang/ibt
공식 코드Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
https://github.com/jmiemirza/mate
공식 코드https://github.com/kabouzeid/point2vec
공식 코드point2vec for Self-Supervised Representation Learning on Point Clouds
https://github.com/kaist-avelab/PointStack
공식 코드Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
https://github.com/kanezaki/rotationnet
공식 코드https://github.com/kimianoorbakhsh/lpf-defence
공식 코드LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
https://github.com/kimianoorbakhsh/lpf-defense
공식 코드LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
https://github.com/layumi/person-reid-3d
공식 코드Parameter-Efficient Person Re-identification in the 3D Space
https://github.com/lifebeyondexpectations/eccv22-pointmixer
공식 코드https://github.com/lightaime/deep_gcns
공식 코드https://github.com/lightaime/deep_gcns_torch
공식 코드https://github.com/lijx10/SO-Net
공식 코드https://github.com/liuyyy111/point-rae
공식 코드Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
https://github.com/lorenmt/vsl
공식 코드https://github.com/lulutang0608/Point-BERT
공식 코드Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
https://github.com/lyqun/FPConv
공식 코드https://github.com/ma-xu/pointmlp-pytorch
공식 코드Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
공식 코드https://github.com/matrix-asc/dela
공식 코드https://github.com/mcg-nju/app-net
공식 코드APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification
https://github.com/mcr-peft/ex-mcr
공식 코드https://github.com/mlvlab/SageMix
공식 코드https://github.com/mlvlab/spotr
공식 코드Self-positioning Point-based Transformer for Point Cloud Understanding
https://github.com/mmmmimic/diffconvnet
공식 코드diffConv: Analyzing Irregular Point Clouds with an Irregular View
https://github.com/mohamedafham/crosspoint
공식 코드CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.