The KITTI Vision Benchmark Suite

Object detection in point clouds is an important
aspect of
many robotics applications such as autonomous
driving. In
this paper we consider the problem of encoding a
point
cloud into a format appropriate for a downstream
detection
pipeline. Recent literature suggests two types of
encoders;
fixed encoders tend to be fast but sacrifice
accuracy, while
encoders that are learned from data are more
accurate, but
slower. In this work we propose PointPillars, a
novel encoder
which utilizes PointNets to learn a representation
of point
clouds organized in vertical columns (pillars).
While the
encoded features can be used with any standard 2D

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