论文标题
事件变压器+。用于有效事件数据处理的多功能解决方案
Event Transformer+. A multi-purpose solution for efficient event data processing
论文作者
论文摘要
事件摄像机记录稀疏照明随着时间分辨率和高动态范围而变化。由于它们稀疏的记录和低消费量，它们越来越多地用于AR/VR和自动驾驶等应用中。当前的优质方法通常会忽略特定的事件数据属性，从而导致通用但计算昂贵的算法的发展，而事件感知方法的性能也不那么执行。我们提出了事件变压器+，它通过基于贴片的事件表示和更强大的骨干来改善我们的开创性工作EVT，以实现更准确的结果，同时仍然受益于事件数据稀疏性，以提高其效率。此外，我们还展示了我们的系统如何与不同的数据模式一起工作并提出特定的输出头，以进行事件流分类（即操作识别）和每个像素预测（密集的深度估计）。评估结果表明，在GPU和CPU上需要最少的计算资源，对最先进的表现更好。
Event cameras record sparse illumination changes with high temporal resolution and high dynamic range. Thanks to their sparse recording and low consumption, they are increasingly used in applications such as AR/VR and autonomous driving. Current topperforming methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms, while event-aware methods do not perform as well. We propose Event Transformer+, that improves our seminal work EvT with a refined patch-based event representation and a more robust backbone to achieve more accurate results, while still benefiting from event-data sparsity to increase its efficiency. Additionally, we show how our system can work with different data modalities and propose specific output heads, for event-stream classification (i.e. action recognition) and per-pixel predictions (dense depth estimation). Evaluation results show better performance to the state-of-the-art while requiring minimal computation resources, both on GPU and CPU.
