[Paper Review] PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis

Zicheng Wang, Zhenghao Chen+4 more|arXiv (Cornell University)|May 24, 2024

3D Surveying and Cultural Heritage7 citations

TL;DR

PoinTramba combines Transformer-based intra-group feature modeling with Mamba-based inter-group processing, guided by a bi-directional importance-aware ordering strategy and importance-aware pooling, achieving state-of-the-art or competitive results on point cloud classification and segmentation with linear-time inter-group computation.

ABSTRACT

Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial. Conversely, the Mamba offers greater efficiency but shows limited potential compared with Transformer-based methods. In this study, we introduce PoinTramba, a pioneering hybrid framework that synergies the analytical power of Transformer with the remarkable computational efficiency of Mamba for enhanced point cloud analysis. Specifically, our approach first segments point clouds into groups, where the Transformer meticulously captures intricate intra-group dependencies and produces group embeddings, whose inter-group relationships will be simultaneously and adeptly captured by efficient Mamba architecture, ensuring comprehensive analysis. Unlike previous Mamba approaches, we introduce a bi-directional importance-aware ordering (BIO) strategy to tackle the challenges of random ordering effects. This innovative strategy intelligently reorders group embeddings based on their calculated importance scores, significantly enhancing Mamba's performance and optimizing the overall analytical process. Our framework achieves a superior balance between computational efficiency and analytical performance by seamlessly integrating these advanced techniques, marking a substantial leap forward in point cloud analysis. Extensive experiments on datasets such as ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our approach, establishing a new state-of-the-art analysis benchmark on point cloud recognition. For the first time, this paradigm leverages the combined strengths of both Transformer and Mamba architectures, facilitating a new standard in the field. The code is available at https://github.com/xiaoyao3302/PoinTramba.

Motivation & Objective

• Motivate efficient point cloud analysis by balancing Transformer accuracy with Mamba efficiency.
• Propose a hybrid architecture that handles intra-group and inter-group dependencies separately.
• Introduce a bi-directional importance-aware ordering (BIO) strategy to mitigate random-ordering effects in Mamba.
• Develop an importance-aware pooling mechanism to form robust global point-cloud features.
• Demonstrate effectiveness on classification and segmentation benchmarks (ScanObjectNN, ModelNet40, ShapeNetPart).

Proposed method

• Segment point clouds into G groups and extract intra-group embeddings using a Transformer encoder.
• Feed group embeddings into an inter-group Mamba encoder to capture global inter-group dependencies with linear complexity.
• Apply a bi-directional importance-aware ordering (BIO) to reorder group embeddings before Mamba processing.
• Predict importance scores for group embeddings to guide reordering and perform importance-aware pooling to form the global feature.
• Train end-to-end with a multi-term loss including task loss, importance loss, and alignment loss.

Experimental results

Research questions

• RQ1 Can a hybrid Transformer-Mamba architecture outperform single-architecture point cloud models in classification and segmentation while maintaining efficiency?
• RQ2 Does bi-directional importance-aware ordering (BIO) improve Mamba’s handling of unordered point clouds?
• RQ3 Does importance-aware pooling effectively leverage reordered group embeddings to produce robust global features?
• RQ4 What is the contribution of each component (Transformer, Mamba, BIO, alignment, IAP) to overall performance?

Key findings

• PoinTramba achieves competitive or superior accuracy to state-of-the-art methods on ScanObjectNN (PB-T50-RS variant: 92.3 ± 0.4; Ours with no rotation: 92.3 ± 0.2; 88.9–89.1 in PB-T50-RS variants).
• On ModelNet40, PoinTramba attains 92.7% (±0.1) with the hybrid backbone, competitive with or surpassing recent Transformer/Mamba methods.
• On ShapeNetPart, PoinTramba achieves an Inst. mIoU of 85.7% (±0.1), comparable to prior SOTA methods.
• Ablation shows inter-group Mamba alone improves accuracy by 8.2% over PointNet++; adding intra-group Transformer adds 0.4%; BIO adds 2.1%; IAP adds 0.5%.
• Alignment loss and BIO interact to further boost performance (alignment: +1.4% for Mamba, +1.7% for PoinTramba).
• BIO ordering outperforms single-directional or random ordering strategies, and IAP outperforms average/max pooling.