Tsuyoshi Takei

Shape-similarity search of three-dimensional models using parameterized statistics

In this paper, we propose a method for shape-similarity search of 3D polygonal-mesh models. The system accepts triangular meshes, but tolerates degenerated polygons, disconnected component, and other anomalies. As the feature vector, the method uses a combination of three vectors, (1) the moment of inertia, (2) the average distance of surface from the axis, and (3) the variance of distance of the surface from the axis. Values in each vector are discretely parameterized along each of the three principal axes of inertia of the model. We employed the Euclidean distance and the elastic-matching distance as the measures of distance between pairs of feature vectors. Experiments showed that the proposed shape features and distance measures perform fairly well in retrieving models having similar shape from a database of VRML models.

Shape-similarity search of 3D models by using enhanced shape functions

We propose a pair of shape features for shape-similarity search of 3D (three-dimensional) polygonal-mesh models. The shape features are an extension of the D2 shape functions proposed by Osada et al. (2001). Our proposed shape features are tolerant of topological variations and geometrical degeneracies. Our shape feature is also invariant to similarity transformation. Experiments showed that, with only a modest increase in computational cost, our shape feature achieved a significant performance improvement over Osadas D2.

Shape similarity comparison of 3D models using alpha shapes

As the number of in-house and public-domain 3D shape models increase, importance of shape-similarity based search and retrieval for 3D shapes models has increased rapidly. In this paper, we describe our preliminary findings in applying a multiresolution analysis technique to the task of shape similarity comparison of polygon soup models. We used the 3D alpha shapes algorithm to create a multiresolution hierarchy of shapes from the given 3D models. We then applied a (single resolution) shape descriptor to each of the models at multiple resolution levels to derive a multiresolution shape descriptor. According to our evaluation experiments, the retrieval performance of our resolution descriptor outperformed its single resolution counterpart, proving the effectiveness of the basic approach.