Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Points to Patches: Enabling the Use of Self-Attention for 3D Shape
  Recognition   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.

14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc