3D point analysis more accurate and efficient with state space than transformer with PointMamba
3D point analysis more accurate and efficient with state space than transformer with PointMamba
PointMamba: A Simple State Space Model for Point Cloud Analysis
arXiv paper abstract https://arxiv.org/abs/2402.10739
arXiv PDF paper https://arxiv.org/pdf/2402.10739.pdf
Transformers ... one of the foundational architectures in point cloud analysis ... However, the attention mechanism has quadratic complexity and is difficult to extend to long sequence modeling
... Recently, state space models (SSM), a new family of deep sequence models, have presented great potential for sequence modeling in NLP tasks.
... propose PointMamba, a framework with global modeling and linear complexity.
... by taking embedded point patches as input, ... proposed a reordering strategy to enhance SSM's global modeling ability by providing a more logical geometric scanning order.
The reordered point tokens are then sent to a series of Mamba blocks to causally capture the point cloud structure.
... proposed PointMamba outperforms the transformer-based counterparts on different point cloud analysis datasets, while ... saving about 44.3% parameters and 25% FLOPs ...
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