  Real-time semantic segmentation of LiDAR data is crucial for autonomously
driving vehicles, which are usually equipped with an embedded platform and have
limited computational resources. Approaches that operate directly on the point
cloud use complex spatial aggregation operations, which are very expensive and
difficult to optimize for embedded platforms. They are therefore not suitable
for real-time applications with embedded systems. As an alternative,
projection-based methods are more efficient and can run on embedded platforms.
However, the current state-of-the-art projection-based methods do not achieve
the same accuracy as point-based methods and use millions of parameters. In
this paper, we therefore propose a projection-based method, called Multi-scale
Interaction Network (MINet), which is very efficient and accurate. The network
uses multiple paths with different scales and balances the computational
resources between the scales. Additional dense interactions between the scales
avoid redundant computations and make the network highly efficient. The
proposed network outperforms point-based, image-based, and projection-based
methods in terms of accuracy, number of parameters, and runtime. Moreover, the
network processes more than 24 scans per second on an embedded platform, which
is higher than the framerates of LiDAR sensors. The network is therefore
suitable for autonomous vehicles.
