  This paper presents a novel Fully Binary Point Cloud Transformer (FBPT) model
which has the potential to be widely applied and expanded in the fields of
robotics and mobile devices. By compressing the weights and activations of a
32-bit full-precision network to 1-bit binary values, the proposed binary point
cloud Transformer network significantly reduces the storage footprint and
computational resource requirements of neural network models for point cloud
processing tasks, compared to full-precision point cloud networks. However,
achieving a fully binary point cloud Transformer network, where all parts
except the modules specific to the task are binary, poses challenges and
bottlenecks in quantizing the activations of Q, K, V and self-attention in the
attention module, as they do not adhere to simple probability distributions and
can vary with input data. Furthermore, in our network, the binary attention
module undergoes a degradation of the self-attention module due to the uniform
distribution that occurs after the softmax operation. The primary focus of this
paper is on addressing the performance degradation issue caused by the use of
binary point cloud Transformer modules. We propose a novel binarization
mechanism called dynamic-static hybridization. Specifically, our approach
combines static binarization of the overall network model with fine granularity
dynamic binarization of data-sensitive components. Furthermore, we make use of
a novel hierarchical training scheme to obtain the optimal model and
binarization parameters. These above improvements allow the proposed
binarization method to outperform binarization methods applied to convolution
neural networks when used in point cloud Transformer structures. To demonstrate
the superiority of our algorithm, we conducted experiments on two different
tasks: point cloud classification and place recognition.
