Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition
This addresses computational bottlenecks in applying Transformers to 3D point cloud data, offering incremental improvements for tasks like shape classification and scene reconstruction.
The paper tackles the inefficiency and ineffectiveness of self-attention in 3D shape recognition by proposing a two-stage Point Transformer-in-Transformer approach that combines local and global attention, resulting in more useful features for classification and improved computational efficiency.
While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational complexity, the self-attention operator quickly becomes inefficient as the set of input points grows larger. Furthermore, we find that the attention mechanism struggles to find useful connections between individual points on a global scale. In order to alleviate these problems, we propose a two-stage Point Transformer-in-Transformer (Point-TnT) approach which combines local and global attention mechanisms, enabling both individual points and patches of points to attend to each other effectively. Experiments on shape classification show that such an approach provides more useful features for downstream tasks than the baseline Transformer, while also being more computationally efficient. In addition, we also extend our method to feature matching for scene reconstruction, showing that it can be used in conjunction with existing scene reconstruction pipelines.