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

TL;DR

MambaTron introduces an efficient Mamba-Transformer based method for cross-modal point cloud enhancement, leveraging long-sequence processing to achieve competitive performance with reduced computational costs.

Contribution

This work pioneers the application of Mamba-based cross-attention in multi-modal 3D vision tasks, specifically for view-guided point cloud completion.

Findings

Achieves performance comparable to state-of-the-art methods.

Uses significantly fewer computational resources.

Demonstrates effective cross-modal reconstruction capabilities.

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.