3D-OutDet: A Fast and Memory Efficient Outlier Detector for 3D LiDAR
Point Clouds in Adverse Weather
Abstract
Adverse weather conditions such as snow, rain, and fog are natural
phenomena that can impair the performance of the perception algorithms
in autonomous vehicles. Although LiDARs provide accurate and reliable
scans of the surroundings, its output can be substantially degraded by
precipitation (e.g., snow particles) leading to an undesired effect on
the downstream perception tasks. Several studies have been performed to
battle this undesired effect by filtering out precipitation outliers,
however, these works have large memory consumption and long execution
times which are not desired for onboard applications. To that end, we
introduce a novel outlier detector for 3D LiDAR point clouds captured
under adverse weather conditions. Our proposed detector 3D-OutDet is
based on a novel convolution operation that processes nearest neighbors
only, allowing the model to capture the most relevant points. This
reduces the number of layers, resulting in a model with a low memory
footprint and fast execution time, while producing a competitive
performance compared to state-of-the-art models. We conduct extensive
experiments on three different datasets (WADS, SnowyKITTI, and
SemanticSpray) and show that with a sacrifice of 0.16% mIOU
performance, our model reduces the memory consumption by 99.92%, number
of operations by 96.87%, and execution time by 82.84% per point cloud
on the real-scanned WADS dataset. Our experimental evaluations also
showed that the mIOU performance of the downstream semantic segmentation
task on WADS can be improved up to 5.08% after applying our proposed
outlier detector. We release our source code, supplementary material and
videos in https://sporsho.github.io/3DOutDet . Upon clicking the link
you will have to option to go to source code, see supplementary
information and view videos generated with our 3D-OutDet.