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This paper presents a novel architecture for point cloud road user detection,which is based on a classical point cloud proposal generator approach, thatutilizes simple geometrical rules. New methods are coupled with this techniqueto achieve extremely small computational requirement, and mAP that iscomparable to the state-of-the-art. The idea is to specifically exploitgeometrical rules in hopes of faster performance. The typical downsides of thisapproach, e.g. global context loss, are tackled in this paper, and solutionsare presented. This approach allows real-time performance on a single core CPU,which is not the case with end-to-end solutions presented in thestate-of-the-art. We have evaluated the performance of the method with thepublic KITTI dataset, and with our own annotated dataset collected with a smallmobile robot platform. Moreover, we also present a novel ground segmentationmethod, which is evaluated with the public SemanticKITTI dataset.

No GPU? No problem: an ultra fast 3D detection of road users with a simple proposal generator and energy-based out-of-distribution PointNets