Event cameras, or Dynamic Vision Sensor (DVS), are very promising sensors
which have shown several advantages over frame based cameras. However, most
recent work on real applications of these cameras is focused on 3D
reconstruction and 6-DOF camera tracking. Deep learning based approaches, which
are leading the state-of-the-art in visual recognition tasks, could potentially
take advantage of the benefits of DVS, but some adaptations are needed still
needed in order to effectively work on these cameras. This work introduces a
first baseline for semantic segmentation with this kind of data. We build a
semantic segmentation CNN based on state-of-the-art techniques which takes
event information as the only input. Besides, we propose a novel representation
for DVS data that outperforms previously used event representations for related
tasks. Since there is no existing labeled dataset for this task, we propose how
to automatically generate approximated semantic segmentation labels for some
sequences of the DDD17 dataset, which we publish together with the model, and
demonstrate they are valid to train a model for DVS data only. We compare our
results on semantic segmentation from DVS data with results using corresponding
grayscale images, demonstrating how they are complementary and worth combining.

本研究介绍了一种基于深度学习的事件相机语义分割方法，具有比基于灰度图的方法更好的性能，同时提出了一种新的事件相机数据表示方法，并描述了如何利用自动生成的近似语义分割标签对 DDD17 数据集进行训练，从而应对数据集标签不足的问题。