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neurips-2025-point-cloud-synthesis-using-inner-product-transforms-paper-conference
Conference paper (in proceedings)

Point Cloud Synthesis Using Inner Product Transforms

BP2-STS

  • 2026
Published in:
  • 39th Conference on Neural Information Processing Systems (NeurIPS 2025). - 2026
Topological deep learning
Geometric deep learning
Euler characteristic transform
Euler characteristic
Generative models
Point cloud generation
English Point cloud synthesis, i.e. the generation of novel point clouds from an input distribution, remains a challenging task, for which numerous complex machine learning models have been devised. We develop a novel method that encodes geometrical-topological characteristics of point clouds using inner products, leading to a highly-efficient point cloud representation with provable expressivity properties. Integrated into deep learning models, our encoding exhibits high quality in typical tasks like reconstruction, generation, and interpolation, with inference times orders of magnitude faster than existing methods.
Faculty
Faculté des sciences et de médecine
Department
Département d'Informatique
Language
  • English
Classification
Computer science and technology
License
License undefined
Open access status
green
Persistent URL
https://folia.unifr.ch/unifr/documents/336333
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