文章目录

• 1 多传感模式的特点
• 2 深度语义分割
• 3 多模态语义分割
• • 3.1 MULTI-MODAL DATASETS
  • 3.2 多模态语义分割的挑战与问题
• 参考

语义分割的目标：是将一个场景分割成几个有意义的部分，通常是用语义标记图像中的每个像素(pixel-level semantic segmentation)，或者同时检测对象并进行逐像素标记(instance-level semantic segmentation)。
最近，为了统一pixel-level semantic segmentation和instance-level semantic segmentation，提出了全景分割（panoptic segmentation）。

1 多传感模式的特点

1. 视觉和热成像相机：视觉（visual camera）和热成像相机（thermal camera）捕捉到的图像可以提供车辆周围环境的详细纹理信息。视觉相机对光线和天气条件很敏感；热成像相机对白天/夜间的变化更敏感，因为它们能探测到与物体热量有关的红外辐射。然而，这两种类型的相机都不能直接提供深度信息。
2. LIDAR（Light Detection And Ranging）：以三维点的形式给出周围环境的精确深度信息。LIDAR是主动摄影，它测量以一定频率发射的激光束的反射。激光雷达对不同的照明条件受影响较小，而且比视觉相机更少受到各种天气条件的影响，如雾和雨。典型的激光雷达无法捕捉到物体的精细纹理，且当物体距离较远时，激光雷达的点会变得稀疏。
3. Radar(无线电探测和测距)：Radar发射被障碍物反射的电磁波，测量信号运行时间，通过多普勒效应估计物体的径向速度、距离和角度。它们在各种光照和天气条件下都很鲁棒，但由于分辨率低，通过雷达对物体进行分类非常具有挑战性。radar在自适应巡航控制和交通拥堵辅助系统中有着广泛的应用。毫米波（mmWave）是一种短波雷达技术。

2 深度语义分割

深度语义分割的数据集
Cityscape	KITTI	Toronto City
Mapillary远景	ApolloScape

专注于分类的像素级语义分割	【3】/【4】/【5】
专注于路端语义分割	【6】/【7】
专注于不同交通参与者的实例级语义分割	【8】/【9】/【10】
融合全局信息的语义分割	扩张卷积【11】【12】，多尺度预测【13】，以及添加条件随机场(CRFs)作为后处理步骤【14】
专注于语义分割的实时性	从操作(GFLOPs)和推理速度(fps)两个方面对几种语义分词架构的实时性进行了比较研究【15】

3 多模态语义分割

3.1 MULTI-MODAL DATASETS

3.2 多模态语义分割的挑战与问题

参考

1. 自动驾驶深度多模态目标检测和语义分割:数据集、方法和挑战
2. Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
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6. L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using fully convolutional neural networks,” in IEEE Intelligent Vehicles Symp., 2017, pp. 1019–1024.
7. M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, and R. Urtasun, “MultiNet: Real-time joint semantic reasoning for autonomous driving,” in IEEE Intelligent Vehicles Symp., 2018.
8. B. Wu, A. Wan, X. Yue, and K. Keutzer, “SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3d lidar point cloud,” in IEEE Int. Conf. Robotics and Automation, May 2018, pp. 1887–1893.
9. K. He, G. Gkioxari, P. Doll ́ ar, and R. Girshick, “Mask R-CNN,” in Proc. IEEE Conf. Computer Vision, 2017, pp. 2980–2988.
10. J. Uhrig, E. Rehder, B. Fr ̈ ohlich, U. Franke, and T. Brox, “Box2Pix: Single-shot instance segmentation by assigning pixels to object boxes,” in IEEE Intelligent Vehicles Symp., 2018.
11. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 834–848, 2018.
12. A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, “ENet: A deep neural network architecture for real-time semantic segmentation,” arXiv:1606.02147 [cs.CV], 2016.
13. A. Roy and S. Todorovic, “A multi-scale CNN for affordance segmentation in RGB images,” in Proc. Eur. Conf. Computer Vision. Springer, 2016, pp. 186–201.
14. S. Zheng et al., “Conditional random fields as recurrent neural networks,” in Proc. IEEE Conf. Computer Vision, 2015, pp. 1529–1537.
15. M. Siam, M. Gamal, M. Abdel-Razek, S. Yogamani, M. Jagersand, and H. Zhang, “A comparative study of real-time semantic segmentation for autonomous driving,” in Workshop Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018, pp. 587–597.