DeepAI AI Chat
Log In Sign Up

Rethinking PointNet Embedding for Faster and Compact Model

by   Teppei Suzuki, et al.

PointNet, which is the widely used point-wise embedding method and known as a universal approximator for continuous set functions, can process one million points per second. Nevertheless, real-time inference for the recent development of high-performing sensors is still challenging with existing neural network-based methods, including PointNet. In ordinary cases, the embedding function of PointNet behaves like a soft-indicator function that is activated when the input points exist in a certain local region of the input space. Leveraging this property, we reduce the computational costs of point-wise embedding by replacing the embedding function of PointNet with the soft-indicator function by Gaussian kernels. Moreover, we show that the Gaussian kernels also satisfy the universal approximation theorem that PointNet satisfies. In experiments, we verify that our model using the Gaussian kernels achieves comparable results to baseline methods, but with much fewer floating-point operations per sample up to 92% reduction from PointNet.


page 1

page 2

page 3

page 4


Universal Approximation Property of Neural Ordinary Differential Equations

Neural ordinary differential equations (NODEs) is an invertible neural n...

Universal Approximation Under Constraints is Possible with Transformers

Many practical problems need the output of a machine learning model to s...

Universal Horn Sentences and the Joint Embedding Property

The finite models of a universal sentence Φ are the age of a structure i...

End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization

For Bayesian optimization (BO) on high-dimensional data with complex str...

Universal Convergence of Kriging

Kriging based on Gaussian random fields is widely used in reconstructing...

Unsupervised Learning of the Set of Local Maxima

This paper describes a new form of unsupervised learning, whose input is...