  We propose a new method for fusing a LIDAR point cloud and camera-captured
images in the deep convolutional neural network (CNN). The proposed method
constructs a new layer called non-homogeneous pooling layer to transform
features between bird view map and front view map. The sparse LIDAR point cloud
is used to construct the mapping between the two maps. The pooling layer allows
efficient fusion of the bird view and front view features at any stage of the
network. This is favorable for the 3D-object detection using camera-LIDAR
fusion in autonomous driving scenarios. A corresponding deep CNN is designed
and tested on the KITTI bird view object detection dataset, which produces 3D
bounding boxes from the bird view map. The fusion method shows particular
benefit for detection of pedestrians in the bird view compared to other
fusion-based object detection networks.
