The discovery of meaningful parts of a shape is required for many geometry processing applications, such as parameterization, shape correspondence, and animation. It is natural to consider primitives such as spheres, cylinders and cones as the building blocks of shapes, and thus to discover parts by fitting such primitives to a given surface. This approach, however, will break down if primitive parts have undergone almost-isometric deformations, as is the case, for example, for articulated human models. We suggest that parts can be discovered instead by finding intrinsic primitives, which we define as parts that posses an approximate intrinsic symmetry. We employ the recently-developed method of computing discrete approximate Killing vector fields (AKVFs) to discover intrinsic primitives by investigating the relationship between the AKVFs of a composite object and the AKVFs of its parts. We show how to leverage this relationship with a standard clustering method to extract k intrinsic primitives and remaining asymmetric parts of a shape for a given k. We demonstrate the value of this approach for identifying the prominent symmetry generators of the parts of a given shape. Additionally, we show how our method can be modified slightly to segment an entire surface without marking asymmetric connecting regions and compare this approach to state-of-the-art methods using the Princeton Segmentation Benchmark. © 2011 The Author(s).
|Original language||English (US)|
|Title of host publication||Computer Graphics Forum|
|Number of pages||10|
|State||Published - Apr 28 2011|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors would like to acknowledge the following grants: The NSF GRF program, the NDSEG program of the DoD, the Hertz Foundation Fellowship, the Weizmann Institute WiS award, NSF grants FODAVA 0808515, CCF 0808515, an AEA program grant from KAUST, a Google research grant, and a seed grant from the Stanford CS Department.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.