MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling

• URL: http://arxiv.org/abs/2501.16384v1
• Date: Sat, 25 Jan 2025 05:25:20 GMT
• Title: MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling
• Authors: Sai Tarun Inaganti, Gennady Petrenko,
• Abstract summary: Mamba is an efficient alternative to the self-attention mechanism. We introduce MambaTron, a Mamba-Transformer cell that serves as a building block for our network. Our model demonstrates a degree of performance comparable to the current state-of-the-art techniques.
• Score: 0.0
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• Abstract: Point cloud enhancement is the process of generating a high-quality point cloud from an incomplete input. This is done by filling in the missing details from a reference like the ground truth via regression, for example. In addition to unimodal image and point cloud reconstruction, we focus on the task of view-guided point cloud completion, where we gather the missing information from an image, which represents a view of the point cloud and use it to generate the output point cloud. With the recent research efforts surrounding state-space models, originally in natural language processing and now in 2D and 3D vision, Mamba has shown promising results as an efficient alternative to the self-attention mechanism. However, there is limited research towards employing Mamba for cross-attention between the image and the input point cloud, which is crucial in multi-modal problems. In this paper, we introduce MambaTron, a Mamba-Transformer cell that serves as a building block for our network which is capable of unimodal and cross-modal reconstruction which includes view-guided point cloud completion.We explore the benefits of Mamba's long-sequence efficiency coupled with the Transformer's excellent analytical capabilities through MambaTron. This approach is one of the first attempts to implement a Mamba-based analogue of cross-attention, especially in computer vision. Our model demonstrates a degree of performance comparable to the current state-of-the-art techniques while using a fraction of the computation resources.

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