Image: Qi et al

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Point cloud is an important type of geometric data structure.

3,200

# 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.

1,969

# PointCNN: Convolution On X-Transformed Points

We present a simple and general framework for feature learning from point cloud.

1,137

# 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.

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

1,137

# Dynamic Graph CNN for Learning on Point Clouds

24 Jan 2018WangYueFt/dgcnn

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.

912

# KPConv: Flexible and Deformable Convolution for Point Clouds

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

Ranked #1 on Scene Segmentation on ScanNet (3DIoU metric)

448

# 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.

405

# Pct: Point cloud transformer

17 Dec 2020MenghaoGuo/PCT

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

Ranked #8 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)

342

# 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.

Ranked #14 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)

339

# SO-Net: Self-Organizing Network for Point Cloud Analysis

This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.

302