Constrained Planar Cuts - Object Partitioning for Point Clouds

Markus Schoeler, Jeremie Papon, Florentin Worgotter; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5207-5215


While humans can easily separate unknown objects into meaningful parts, recent segmentation methods can only achieve similar partitionings by training on human-annotated ground-truth data. Here we introduce a bottom-up method for segmenting 3D point clouds into functional parts which does not require supervision and achieves equally good results. Our method uses local concavities as an indicator for inter-part boundaries. We show that this criterion is efficient to compute and generalizes well across different object classes. The algorithm employs a novel locally constrained geometrical boundary model which proposes greedy cuts through a local concavity graph. Only planar cuts are considered and evaluated using a cost function, which rewards cuts orthogonal to concave edges. Additionally, a local clustering constraint is applied to ensure the partitioning only affects relevant locally concave regions. We evaluate our algorithm on recordings from an RGB-D camera as well as the Princeton Segmentation Benchmark, using a fixed set of parameters across all object classes. This stands in stark contrast to most reported results which require either knowing the number of parts or annotated ground-truth for learning. Our approach outperforms all existing bottom-up methods (reducing the gap to human performance by up to 50%) and achieves scores similar to top-down data-driven approaches.

Related Material

author = {Schoeler, Markus and Papon, Jeremie and Worgotter, Florentin},
title = {Constrained Planar Cuts - Object Partitioning for Point Clouds},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}