K. Wu, M.D. Levine Object part descriptions are important because they reflect natural structures in the real world and support efficient object recognition. Part segmentation is the crucial step for building such a description. In this research, we propose a novel approach to part segmentation using multiview range images. Object data obtained from multiple viewpoints contain much more shape information about a complicated object than data taken from a single view. Various techniques for multiview integration and shape reconstruction have been published for many years. However, progress towards achieving efficient and accurate high-level descriptions has been slow. Voxel-based methods, which use an explicit 3D coordinate system, are usually employed but these are inefficient with respect to computer time and memory. Tessellating an objects surface into a triangular mesh provides an implicit coordinate system, but this has also proved difficult to achieve. Thus shape analysis based on triangular meshes has rarely been presented in the literature. Motivated by physics, we compute the simulated charge density distribution over an object surface which is tessellated by a triangular mesh. We then detect deep surface concavities by locating local charge density minima. The object is then segmented into parts at these surface concavities. Although this approach deals with a complete 3D object, it performs the reconstruction only on the object surface, rather than its interior as would the commonly used voxel-based approaches. Thus, it requires much less unknowns. Our method can also be extended to perform part segmentation using single-view range data and can be generalized to take any polygonal mesh as input. A significant aspect of this work is that it computes surface features using global, rather than local, data. In addition, it avoids explicitly dealing with the scale problem, a very difficult issue in the usual curvature-based approaches. The feature that distinguishes this approach from others is the application of simulated charge density distributions. The results can also be used for performing other tasks, such as edge and corner detection.