Self-positioning Point-based Transformer for Point Cloud Understanding (2303.16450v1)

Abstract: Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity. Specifically, this architecture consists of local self-attention and self-positioning point-based global cross-attention. The self-positioning points, adaptively located based on the input shape, consider both spatial and semantic information with disentangled attention to improve expressive power. With the self-positioning points, we propose a novel global cross-attention mechanism for point clouds, which improves the scalability of global self-attention by allowing the attention module to compute attention weights with only a small set of self-positioning points. Experiments show the effectiveness of SPoTr on three point cloud tasks such as shape classification, part segmentation, and scene segmentation. In particular, our proposed model achieves an accuracy gain of 2.6% over the previous best models on shape classification with ScanObjectNN. We also provide qualitative analyses to demonstrate the interpretability of self-positioning points. The code of SPoTr is available at https://github.com/mlvlab/SPoTr.

• The paper introduces SPoTr, a Transformer model that uses self-positioning points to efficiently capture both local and global shape contexts in point clouds.
• It combines local points attention and self-positioning attention modules to reduce computational complexity while enhancing feature extraction.
• Experimental results demonstrate a 2.6% accuracy gain in shape classification and superior performance on segmentation benchmarks.

Self-positioning Point-based Transformer for Point Cloud Understanding

Point cloud understanding has emerged as a pivotal area within computer vision, particularly with applications in autonomous driving, robotics, and augmented reality. The challenges posed by point clouds stem from their unordered nature and irregular structure, making traditional convolutional approaches less effective. The proposed paper introduces a novel architecture, the Self-positioning Point-based Transformer (SPoTr), aimed at efficiently capturing both local and global shape contexts while mitigating the scaling complexity often associated with Transformer models.

The SPoTr framework leverages two distinct modules: the local points attention (LPA) and self-positioning point-based attention (SPA). SPA distinguishes itself through the use of self-positioning points (SP points), which are adaptively placed within the point cloud to represent salient features effectively. This adaptivity allows the attention mechanism to compute global cross-attention efficiently using a small set of SP points, rather than necessitating quadratic computation over entire sets of points. By utilizing disentangled attention in SPA, spatial and semantic proximities are independently considered, enhancing the descriptive power of the representation and allowing SP points to suppress semantically irrelevant information.

One of the key results of the paper is the significant accuracy improvement demonstrated across various point cloud tasks. In the shape classification task using the ScanObjectNN dataset, SPoTr achieves an accuracy gain of 2.6% over previous models, underscoring the importance of capturing long-range shape contexts in real-world 3D data. The paper further validates the effectiveness of the SPoTr architecture through extensive experiments on segmentation datasets, including SN-Part and S3DIS, where it consistently achieves superior performance compared to existing benchmarks.

The implications of this research are manifold. From a theoretical perspective, SPoTr bridges the scalability gap in applying Transformer models to point cloud data, offering a robust method to capture comprehensive shape information without prohibitive computational costs. This opens up new avenues for exploring attention-based methodologies in sparse and irregular data forms. Practically, the deployment of SPoTr can enhance multi-object recognition scenarios, providing improved accuracy and efficiency in real-time processing tasks such as autonomous driving or robotic navigation.

The qualitative analyses presented in the paper further elucidate the interpretability of SPoTr. Visualizations of SP points across different object categories reveal a consistent placement pattern that correlates with semantic meanings within categories. This indicates the potential of SPoTr to not only capture relevant features but also reflect meaningful spatial semantic relations, which are crucial for accurate interpretation in complex environments.

Future developments may explore augmentations to the SPoTr architecture, such as integrating additional feature channels to further refine interpretability or expanding the scope to handle dynamic point cloud data. There is also potential for exploring hybrid models that incorporate SPoTr with other machine learning paradigms for enhanced feature extraction and context understanding.

In conclusion, the paper presents significant advancements in the understanding and processing of point cloud data through SPoTr. Its approach to handling long-range dependencies efficiently opens new perspectives in both theoretical exploration and practical application, paving the way for enriched capabilities in point cloud-based systems.