Point cloud segmentation using hierarchical tree for architectural models
This addresses the challenge of point cloud segmentation in architecture, but it is incremental as it builds on existing primitive-based methods.
The paper tackles the problem of segmenting 3D point clouds for architectural models by proposing a primitive-based algorithm using a hierarchical tree, achieving over 90% accuracy for domes and minarets.
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.