MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915206095962112 |
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| author | Inaganti, Sai Tarun Petrenko, Gennady |
| author_facet | Inaganti, Sai Tarun Petrenko, Gennady |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling Inaganti, Sai Tarun Petrenko, Gennady Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.16384 |