A spatially enhanced network with camera-lidar fusion for 3D semantic segmentation.

In autonomous vehicle technology, environmental perception is a very important part of the whole system. The robustness and accuracy of the perception determine the performance of the whole system. Compared with the camera, lidar can generate point cloud data which can provide accurate 3D information better. At present, more researches focus on environmental perception through point cloud. However, the point cloud has the characteristics of sparsity, unordered and non-uniform distribution, its processing method is also different from the general image-based method. In this paper, we proposed a method of semantic segmentation of point cloud which is realized by convolutional neural network. Through spherical mapping, the scattered point cloud is transformed into 2D spherical map with dense and uniform distribution, which is convenient for the processing of the network. To reduce the loss of point information, the spatial position, reflection intensity information and angle value of point are preserved during spherical mapping. Our work includes a lightweight convolutional neural network that fuses point cloud and image information. In addition, a spatial module is added to supplement the loss of spatial information and improve the performance of segmentation. Finally, a series of comparative experiments and performance evaluation are carried out, which show that the proposed method is not only much better than the original structure, but also better than another image and lidar fusion network in the segmentation of each category. [ABSTRACT FROM AUTHOR]