3D Point Cloud Classification

58 papers with code • 3 benchmarks • 3 datasets

Image: Qi et al

Greatest papers with code

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

yangyanli/PointCNN NeurIPS 2018

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

 Ranked #1 on 3D Instance Segmentation on S3DIS (mIoU metric)

3D Instance Segmentation 3D Part Segmentation +1

Dynamic Graph CNN for Learning on Point Clouds

WangYueFt/dgcnn 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.

3D Part Segmentation 3D Point Cloud Classification

Perceiver: General Perception with Iterative Attention

lucidrains/perceiver-pytorch 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.

3D Point Cloud Classification Audio Classification +1

PCT: Point cloud transformer

MenghaoGuo/PCT 17 Dec 2020

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

Ranked #10 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)

3D Part Segmentation 3D Point Cloud Classification

PointConv: Deep Convolutional Networks on 3D Point Clouds

DylanWusee/pointconv CVPR 2019

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.

3D Part Segmentation 3D Point Cloud Classification +1