# 3D Point Cloud Classification

94 papers with code • 5 benchmarks • 5 datasets

Image: Qi et al

## Libraries

Use these libraries to find 3D Point Cloud Classification models and implementations
3 papers
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# PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.

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

The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.

14

# Dynamic Graph CNN for Learning on Point Clouds

24 Jan 2018

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.

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# Point Transformer

For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.

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# Perceiver: General Perception with Iterative Attention

4 Mar 2021

The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing vision models.

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# PointConv: Deep Convolutional Networks on 3D Point Clouds

Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.

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# PCT: Point cloud transformer

17 Dec 2020

It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.

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# KPConv: Flexible and Deformable Convolution for Point Clouds

Furthermore, these locations are continuous in space and can be learned by the network.

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# Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

28 Jan 2022

Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.

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# Relation-Shape Convolutional Neural Network for Point Cloud Analysis

Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.

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