融合事件相机与激光雷达数据，用注意力机制实现高精度稠密深度估计
DELTA: Dense Depth from Events and LiDAR using Transformer's Attention
- 利用自注意力和交叉注意力建模事件与激光雷达的时空关联
- 在近距离场景下误差降低至之前最先进方法的1/4
- 适合自动驾驶、机器人等需要高精度实时深度感知的场景
事件相机和激光雷达提供互补但不同的数据：前者异步检测光照变化，后者以固定频率提供稀疏但精确的深度信息。目前极少研究探索这两种模态的结合。本文提出一种基于神经网络的新方法，用于融合事件与激光雷达数据以估计稠密深度图。所提出的架构DELTA利用自注意力和交叉注意力机制，建模事件与激光雷达数据内部及之间的空间与时间关系。经过全面评估，结果表明DELTA在事件驱动深度估计任务上达到新最优性能，且在近距离范围内误差相比先前最先进方法最多降低四倍。
原文摘要 · Abstract (English)
Event cameras and LiDARs provide complementary yet distinct data: respectively, asynchronous detections of changes in lighting versus sparse but accurate depth information at a fixed rate. To this day, few works have explored the combination of these two modalities. In this article, we propose a novel neural-network-based method for fusing event and LiDAR data in order to estimate dense depth maps. Our architecture, DELTA, exploits the concepts of self- and cross-attention to model the spatial and temporal relations within and between the event and LiDAR data. Following a thorough evaluation, we demonstrate that DELTA sets a new state of the art in the event-based depth estimation problem, and that it is able to reduce the errors up to four times for close ranges compared to the previous SOTA.
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