Neural multi-view segmentation-aggregation for joint Lidar and image object detection

Abstract

Combining different types of data from multiple views makes it easier to perform object detection. Our novel method enables multi-view deep convolutional neural networks to combine color information from panoramic images and depth information derived from Lidar point clouds for improved street furniture detection. Our focus is on the prediction of world positions of light poles specifically. In contrast to related methods, our method operates on data from real world environments consisting of many complex objects and supports the combination of information from recording locations that do not have fixed relative positions.