The PointMamba framework and the proposed reordering strategy can be applied to various types of non-causal data beyond point clouds to enhance modeling capabilities. One potential application could be in sequential data from time-series analysis, where the ordered sequence of events plays a crucial role in understanding patterns and trends. By adapting the reordering strategy to time-series data, PointMamba could effectively capture the temporal dependencies and relationships within the data, leading to improved modeling performance.
Furthermore, natural language processing tasks, such as text generation and sentiment analysis, could also benefit from the PointMamba framework. By reordering the input tokens based on linguistic structures or semantic relationships, PointMamba could better capture the contextual information in the text and improve the model's ability to generate coherent and meaningful outputs. The reordering strategy could help the model understand the sequential nature of language data and enhance its performance on various NLP tasks.