Chamber Recognition in Cave Data Sets

Abstract

Quantitative analysis of cave systems represented as 3D models is becoming more and more important in the field of cave sciences. One open question is the rigorous identification of chambers in a data set, which has a deep impact on subsequent analysis steps such as size calculation. This affects the international recognition of a cave since especially record-holding caves bear significant tourist attraction potential. In the past, chambers have been identified manually, without any clear definition or guidance. While experts agree on core parts of chambers in general, their opinions may differ in more controversial areas. Since this process is heavily subjective, it is not suited for objective quantitative comparison of caves. Therefore, we present a novel fully-automatic curve skeleton-based chamber recognition algorithm that has been derived from requirements from field experts. We state the problem as a binary labeling problem on a curve skeleton and find a solution through energy minimization. A thorough evaluation of our results with the help of expert feedback showed that our algorithm matches real-world requirements very closely and is thus suited as the foundation for any quantitative cave analysis system.