Point Cloud Upsampling through Patch-based Frequency Superposition

Ritthaler M, Hussian A, Belagiannis V, Kaup A (2026)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

Event location: Bruges

Abstract

In recent years, neural networks have become the dominant models in most point cloud upsampling methods. Although these approaches are achieving good results, they do have drawbacks, such as a lack of interpretability and data dependency. Moreover, they have to be trained on a dataset that is similar to the test data in order to perform well. To avoid these disadvantages, we propose Point Cloud Upsampling through Patch-based Frequency Superposition (PUtPFS), an optimization-based approach that selects subsets of points and estimates the surface of this set through superpositioning spatial frequencies. Then, new points are placed on this surface. By successively selecting points in the least dense regions of the point cloud, a uniform upsampling can be reached. With this method, we surpass the current best upsampling results in the commonly considered point-to-surface distance. Furthermore, we achieve the best Chamfer and Hausdorff distance among the optimization-based approaches. As an additional advantage, our method does not need any training data and is mathematically interpretable.

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How to cite

APA:

Ritthaler, M., Hussian, A., Belagiannis, V., & Kaup, A. (2026). Point Cloud Upsampling through Patch-based Frequency Superposition. In Proceedings of the European Conference on Signal Processing (EUSIPCO). Bruges, BE.

MLA:

Ritthaler, Marina, et al. "Point Cloud Upsampling through Patch-based Frequency Superposition." Proceedings of the European Conference on Signal Processing (EUSIPCO), Bruges 2026.

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