Perception-based shape retrieval for 3D building models

Abstract

With the help of 3D search engines, a large number of 3D building models can be retrieved freely online. A serious disadvantage of most rotation-insensitive shape descriptors is their inability to distinguish between two 3D building models which are different at their main axes, but appear similar when one of them is rotated. To resolve this problem, we present a novel upright-based normalization method which not only correctly rotates such building models, but also greatly simplifies and accelerates the abstraction and the matching of building models’ shape descriptors. Moreover, the abundance of architectural styles significantly hinders the effective shape retrieval of building models. Our research has shown that buildings with different designs are not well distinguished by the widely recognized shape descriptors for general 3D models. Motivated by this observation and to further improve the shape retrieval quality, a new building matching method is introduced and analyzed based on concepts found in the field of perception theory and the well-known Light Field descriptor. The resulting normalized building models are first classified using the qualitative shape descriptors of Shell and Unevenness which outline integral geometrical and topological information. These models are then put in on orderly fashion with the help of an improved quantitative shape descriptor which we will term as Horizontal Light Field Descriptor, since it assembles detailed shape characteristics. To accurately evaluate the proposed methodology, an enlarged building shape database which extends previous well-known shape benchmarks was implemented as well as a model retrieval system supporting inputs from 2D sketches and 3D models. Various experimental performance evaluation results have shown that, as compared to previous methods, retrievals employing the proposed matching methodology are faster and more consistent with human recognition of spatial objects. In addition these performance evaluation results have verified that the proposed methodology does not sacrifice the matching accuracy while significantly improves the efficiency when matching 3D building models.