Few-Shot 3D Point Cloud Classification
25 papers with code • 8 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Few-Shot 3D Point Cloud Classification
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Libraries
Use these libraries to find Few-Shot 3D Point Cloud Classification models and implementations
ModelNet
Most implemented papers
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
qizekun/ShapeLLM
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This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
lulutang0608/Point-BERT
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CVPR 2022
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
zrrskywalker/i2p-mae
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CVPR 2023
Pre-training by numerous image data has become de-facto for robust 2D representations.
PointCNN: Convolution On X-Transformed Points
yangyanli/PointCNN
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NeurIPS 2018
We present a simple and general framework for feature learning from point cloud.
Self-Supervised Few-Shot Learning on Point Clouds
charusharma1991/SSL_PointClouds
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NeurIPS 2020
We present a comprehensive empirical evaluation of our method on both downstream classification and segmentation tasks and show that supervised methods pre-trained with our self-supervised learning method significantly improve the accuracy of state-of-the-art methods.
Unsupervised Point Cloud Pre-Training via Occlusion Completion
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
mohamedafham/crosspoint
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CVPR 2022
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
Masked Discrimination for Self-Supervised Learning on Point Clouds
Masked autoencoding has achieved great success for self-supervised learning in the image and language domains.
Point2Vec for Self-Supervised Representation Learning on Point Clouds
Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach.
A Closer Look at Few-Shot 3D Point Cloud Classification
cgye96/a_closer_look_at_3dfsl
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In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization for novel classes.
