Few-Shot 3D Point Cloud Classification
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Benchmarks
ModelNet40 10-way (10-shot)
ModelNet40 10-way (20-shot)
ModelNet40 5-way (10-shot)
ModelNet40 5-way (20-shot)
Most implemented
Attention Is All You Need
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Dynamic Graph CNN for Learning on Point Clouds
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
Papers
Rethinking Masked Representation Learning for 3D Point Cloud Understanding
Self-supervised point cloud representation learning aims to acquire robust and general feature representations from unlabeled data. Recently, masked point modeling-based methods have shown significant performance improve…
3D Part Segmentation3D Point Cloud ClassificationFew-Shot 3D Point Cloud ClassificationRepresentation Learning3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
Invariance-based and generative methods have shown a conspicuous performance for 3D self-supervised representation learning (SSRL). However, the former relies on hand-crafted data augmentations that introduce bias not un…
3D Part Segmentation3D Point Cloud ClassificationDecoderFew-Shot 3D Point Cloud Classification+1PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Masked autoencoder has been widely explored in point cloud self-supervised learning, whereby the point cloud is generally divided into visible and masked parts. These methods typically include an encoder accepting visibl…
3D Object Classification3D Point Cloud ClassificationDecoderFew-Shot 3D Point Cloud Classification+4Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
Recent advancements in self-supervised learning in the point cloud domain have demonstrated significant potential. However, these methods often suffer from drawbacks, including lengthy pre-training time, the necessity of…
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationClassification+2ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
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. ShapeLLM is built upon…
3D geometry3D Object Captioning3D Point Cloud Classification3D Point Cloud Linear Classification+13Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods le…
3D Point Cloud ClassificationFew-Shot 3D Point Cloud Classification