论文标题
使用双暹罗网络对LIDAR和摄像机数据进行深度无监督的共同表示学习
Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks
论文作者
论文摘要
传感器模式的域间隙对自主机器人的设计构成了挑战。迈向缩小这一差距的一步，我们提出了两个无监督的培训框架，以查找LiDAR和相机数据的共同表示。第一种方法利用双暹罗训练结构来确保结果的一致性。第二种方法使用巧妙的边缘图像将网络引导到所需的表示。所有网络均以无监督的方式进行培训，为可扩展性留出空间。使用常见的计算机视觉应用评估结果，并概述了所提出方法的局限性。
Domain gaps of sensor modalities pose a challenge for the design of autonomous robots. Taking a step towards closing this gap, we propose two unsupervised training frameworks for finding a common representation of LiDAR and camera data. The first method utilizes a double Siamese training structure to ensure consistency in the results. The second method uses a Canny edge image guiding the networks towards a desired representation. All networks are trained in an unsupervised manner, leaving room for scalability. The results are evaluated using common computer vision applications, and the limitations of the proposed approaches are outlined.
