STREAM: A Universal State-Space Model for Sparse Geometric Data

Understand

Handling sparse and unstructured geometric data, such as point clouds or event-based vision, is a pressing challenge in the field of machine vision.

• Recently, sequence models such as Transformers and state-space models entered the domain of geometric data.
• These methods require specialized preprocessing to create a sequential view of a set of points.
• Furthermore, prior works involving sequence models iterate geometric data with either uniform or learned step sizes, implicitly relying on the model to infer the underlying geometric structure.