Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area. A number of techniques have set precedent of either planar or primitive based segmentation in literature. In this work, we present a novel and an effective primitive based point cloud segmentation algorithm. The primary focus, i.e. the main technical contribution of our method is a hierarchical tree which iteratively divides the point cloud into segments. This tree uses an exclusive energy function and a 3D convolutional neural network, HollowNets to classify the segments. We test the efficacy of our proposed approach using both real and synthetic data obtaining an accuracy greater than 90% for domes and minarets.
@article{arxiv.1806.08572,
title = {Point cloud segmentation using hierarchical tree for architectural models},
author = {Omair Hassaan and Abeera Shamail and Zain Butt and Murtaza Taj},
journal= {arXiv preprint arXiv:1806.08572},
year = {2018}
}
Comments
9 pages. 10 figures. Submitted in EuroGraphics 2018