  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.
