Robust 3D Object Detection in Cold Weather Conditions. (arXiv:2205.11925v1 [cs.CV])

Adverse weather conditions can negatively affect LiDAR-based object
detectors. In this work, we focus on the phenomenon of vehicle gas exhaust
condensation in cold weather conditions. This everyday effect can influence the
estimation of object sizes, orientations and introduce ghost object detections,
compromising the reliability of the state of the art object detectors. We
propose to solve this problem by using data augmentation and a novel training
loss term. To effectively train deep neural networks, a large set of labeled …

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