Deep Parametric Continuous Convolutional Neural Networks

Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, Raquel Urtasun
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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
more » ... trary 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.
doi:10.1109/cvpr.2018.00274 dblp:conf/cvpr/WangSMPU18 fatcat:augqqmf56vcqtcof3bi2x64m6e