You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In this section, we show generalizability of our approach to non-Euclidean space. In non-rigid shape classification (Fig. 7), a good classifier should be able to classify (a) and (c) in Fig. 7 correctly as the same category even given their difference in pose, which requires knowledge of intrinsic structure.Shapes in SHREC15 are 2D surfaces embedded in 3D space. Geodesic distances along the surfaces naturally induce a metric space. We show through experiments that adopting PointNet++ in this metric space is an effective way to capture intrinsic structure of the underlying point set.

Question: Is it right to define partition in Euclidean space?
[The paper shows the generalizability of its approach to non-Euclidean space as well. For example, shapes in SHREC15 are 2D surfaces embedded in 3D space. Geodesic distances along the surfaces naturally induce a metric space. The paper shows through experiments that adopting PointNet++ in this metric space is an effective way to capture intrinsic structure of the underlying point set. Hence defining partitions in Euclidean space is not a wrong choice as it doesn't affect the generalizability of the paper's approach.]