# 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## Most implemented papers

# 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.

# 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.

# Dynamic Graph CNN for Learning on Point Clouds

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.

# 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.

# Perceiver: General Perception with Iterative Attention

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.

# 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.

# PCT: Point cloud transformer

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

# KPConv: Flexible and Deformable Convolution for Point Clouds

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

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

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

# 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.