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)，或同时检测对象并标记逐像素(instance-level semantic segmentation)。
最近，为了统一pixel-level semantic segmentation和instance-level semantic segmentation，提出全景分割（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）捕获的图像可以为车辆周围环境提供详细的纹理信息。视觉相机对光线和天气条件非常敏感；热成像相机对白天/晚上的变化更敏感，因为它们可以检测到与物体热量相关的红外辐射。然而，这两种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准确深度信息。LIDAR它是一种主动摄影，它测量了一定频率发射的激光束的反射。激光雷达对不同照明条件的影响较小，受雾、雨等各种天气条件的影响较小。典型的激光雷达无法捕捉到物体的精细纹理，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通过多普勒效应估计物体的径向速度、距离和角度，发射被障碍物反射的电磁波，测量信号的运行时间。它们在各种光照和天气条件下都很好，但由于分辨率低，通过雷达对物体进行分类是非常具有挑战性的。radar广泛应用于自适应巡航控制和交通拥堵辅助系统。（mmWave）是短波雷达技术。

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

像素级语义分割专注于分类	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
语义分割信息的语义分割	扩展卷积1112，多尺度预测13，随机场增加条件(CRFs)后处理步骤14
注重语义分割的实时性	从操作(GFLOPs)和推理速度(fps)对比研究了15几种语义分词架构的实时性

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和问题

参考

1. 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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5. LV. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., no. 12, pp. 2481–2495, 2017.
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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.
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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.
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