While deep learning-based methods have demonstrated outstanding results in
numerous domains, some important functionalities are missing. Resolution
scalability is one of them. In this work, we introduce a novel architecture,
dubbed RESSCAL3D, providing resolution-scalable 3D semantic segmentation of
point clouds. In contrast to existing works, the proposed method does not
require the whole point cloud to be available to start inference. Once a
low-resolution version of the input point cloud is available, first semantic
predictions can be generated in an extremely fast manner. This enables early
decision-making in subsequent processing steps. As additional points become
available, these are processed in parallel. To improve performance, features
from previously computed scales are employed as prior knowledge at the current
scale. Our experiments show that RESSCAL3D is 31-62% faster than the
non-scalable baseline while keeping a limited impact on performance. To the
best of our knowledge, the proposed method is the first to propose a
resolution-scalable approach for 3D semantic segmentation of point clouds based
on deep learning.

通过深度学习技术，我们提出了一种新的架构 RESCAL3D，用于对点云进行分辨率可扩展的三维语义分割，该方法在实现极快的推断速度的同时，保持了较高的性能水平。