Log In Sign Up

Deep Parametric Continuous Convolutional Neural Networks

by   Shenlong Wang, et al.

Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applicability to many real-world applications. In this paper we propose Parametric Continuous Convolution, a new learnable operator that operates over non-grid structured data. The key idea is to exploit parameterized kernel functions that span the full continuous vector space. This generalization allows us to learn over arbitrary data structures as long as their support relationship is computable. Our experiments show significant improvement over the state-of-the-art in point cloud segmentation of indoor and outdoor scenes, and lidar motion estimation of driving scenes.


page 6

page 8

page 11

page 13

page 15

page 16

page 17

page 18


QCNN: Quadrature Convolutional Neural Network with Application to Unstructured Data Compression

We present a new convolution layer for deep learning architectures which...

Interpolated Convolutional Networks for 3D Point Cloud Understanding

Point cloud is an important type of 3D representation. However, directly...

CKConv: Learning Feature Voxelization for Point Cloud Analysis

Despite the remarkable success of deep learning, optimal convolution ope...

A Generalization of Convolutional Neural Networks to Graph-Structured Data

This paper introduces a generalization of Convolutional Neural Networks ...

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation

The research community has observed a massive success of convolutional n...

Code Repositories


A list of references on lidar point cloud processing for autonomous driving

view repo