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
Attention Is All You Need
tensorflow/tensor2tensor
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
charlesq34/pointnet
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CVPR 2017
Point cloud is an important type of geometric data structure.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
yanx27/Pointnet_Pointnet2_pytorch
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Dynamic Graph CNN for Learning on Point Clouds
WangYueFt/dgcnn
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Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
yangyanli/PointCNN
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NeurIPS 2018
The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
Masked Autoencoders for Point Cloud Self-supervised Learning
Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point patches.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
By fine-tuning on downstream tasks, Point-M2AE achieves 86. 43% accuracy on ScanObjectNN, +3. 36% to the second-best, and largely benefits the few-shot classification, part segmentation and 3D object detection with the hierarchical pre-training scheme.
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
runpeidong/act
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The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages.
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
qizekun/ReCon
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This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
zyh16143998882/iccv23-idpt
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ICCV 2023
To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models.
