Point Cloud Classification
114 papers with code • 2 benchmarks • 2 datasets
Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
Benchmarks
These leaderboards are used to track progress in Point Cloud Classification
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Libraries
Use these libraries to find Point Cloud Classification models and implementations
PointCloud-C
JetClass
Most implemented papers
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.
PCT: Point cloud transformer
MenghaoGuo/PCT
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It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks.
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Yochengliu/Relation-Shape-CNN
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CVPR 2019
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.
Benchmarking and Analyzing Point Cloud Classification under Corruptions
3D perception, especially point cloud classification, has achieved substantial progress.
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
We propose a spherical kernel for efficient graph convolution of 3D point clouds.
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
mutianxu/GDANet
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GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components.
