Flex-Convolution

Million-Scale Point-Cloud Learning Beyond Grid-Worlds

Fabian Groh, Patrick Wieschollek and Hendrik P. A. Lensch
University of Tübingen
Asian Conference on Computer Vision (ACCV) 2018

Abstract

Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point clouds containing irregular neighborhoods constantly breaks the grid-based data assumption. Therefore applying best-practices and design choices from 2D-image learning methods towards processing point clouds are not readily possible. In this work, we introduce a natural generalization flex-convolution of the conventional convolution layer along with an efficient GPU implementation. We demonstrate competitive performance on rather small benchmark sets using fewer parameters and lower memory consumption and obtain significant improvements on a million-scale real-world dataset. Ours is the first which allows to efficiently process 7 million points concurrently.

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Bibtex

@inproceedings{accv2018/Groh,
    author = {Fabian Groh and Patrick Wieschollek and Hendrik P. A. Lensch },
    title = {Flex-Convolution (Million-Scale Pointcloud Learning Beyond Grid-Worlds)},
    booktitle = {Asian Conference on Computer Vision (ACCV)},
    month = {Dezember},
    year = {2018}
}