  Semantic segmentation of 3D point cloud data is essential for enhanced
high-level perception in autonomous platforms. Furthermore, given the
increasing deployment of LiDAR sensors onboard of cars and drones, a special
emphasis is also placed on non-computationally intensive algorithms that
operate on mobile GPUs. Previous efficient state-of-the-art methods relied on
2D spherical projection of point clouds as input for 2D fully convolutional
neural networks to balance the accuracy-speed trade-off. This paper introduces
a novel approach for 3D point cloud semantic segmentation that exploits
multiple projections of the point cloud to mitigate the loss of information
inherent in single projection methods. Our Multi-Projection Fusion (MPF)
framework analyzes spherical and bird's-eye view projections using two separate
highly-efficient 2D fully convolutional models then combines the segmentation
results of both views. The proposed framework is validated on the SemanticKITTI
dataset where it achieved a mIoU of 55.5 which is higher than state-of-the-art
projection-based methods RangeNet++ and PolarNet while being 1.6x faster than
the former and 3.1x faster than the latter.
