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Semantic segmentation of 3D point cloud data is essential for enhancedhigh-level perception in autonomous platforms. Furthermore, given theincreasing deployment of LiDAR sensors onboard of cars and drones, a specialemphasis is also placed on non-computationally intensive algorithms thatoperate on mobile GPUs. Previous efficient state-of-the-art methods relied on2D spherical projection of point clouds as input for 2D fully convolutionalneural networks to balance the accuracy-speed trade-off. This paper introducesa novel approach for 3D point cloud semantic segmentation that exploitsmultiple projections of the point cloud to mitigate the loss of informationinherent in single projection methods. Our Multi-Projection Fusion (MPF)framework analyzes spherical and bird's-eye view projections using two separatehighly-efficient 2D fully convolutional models then combines the segmentationresults of both views. The proposed framework is validated on the SemanticKITTIdataset where it achieved a mIoU of 55.5 which is higher than state-of-the-artprojection-based methods RangeNet++ and PolarNet while being 1.6x faster thanthe former and 3.1x faster than the latter.

Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds