Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation
arxiv(2024)
摘要
LiDAR semantic segmentation frameworks predominantly leverage geometry-based
features to differentiate objects within a scan. While these methods excel in
scenarios with clear boundaries and distinct shapes, their performance declines
in environments where boundaries are blurred, particularly in off-road
contexts. To address this, recent strides in 3D segmentation algorithms have
focused on harnessing raw LiDAR intensity measurements to improve prediction
accuracy. Despite these efforts, current learning-based models struggle to
correlate the intricate connections between raw intensity and factors such as
distance, incidence angle, material reflectivity, and atmospheric conditions.
Building upon our prior work, this paper delves into the advantages of
employing calibrated intensity (also referred to as reflectivity) within
learning-based LiDAR semantic segmentation frameworks. We initially establish
that incorporating reflectivity as an input enhances the existing LiDAR
semantic segmentation model. Furthermore, we present findings that enable the
model to learn to calibrate intensity can boost its performance. Through
extensive experimentation on the off-road dataset Rellis-3D, we demonstrate
notable improvements. Specifically, converting intensity to reflectivity
results in a 4
to using raw intensity in Off-road scenarios. Additionally, we also investigate
the possible benefits of using calibrated intensity in semantic segmentation in
urban environments (SemanticKITTI) and cross-sensor domain adaptation.
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