# Fast 3D Semantic Mapping in Road Scenes +
## 2 Related Work  
Our work is motivated by [10] which contributes an indoor 3D semantic SLAM from the RGB-D input. It aims towards a dense 3D map based on ElasticFusion SLAM [14] with semantic labeling. Pixel-wise semantic information is acquired from a Deconvolutional semantic segmentation network [15] using the scaled RGB information and the depth as the input. Depth information is also used to update surfel's depth and normal information to construct 3D dense map during loop closure. In addition, a previous work, SLAM++ [16], creates a map with semantically defined objects, but it is limited to predefined database and hand-crafted template models. In this paper, we make use of an incremental Bayesian fusion strategy with state-of-the-art visual SLAM and semantic segmentation. Visual SLAM usually contains sparse, semi-dense, and dense types depending on the methods of image alignment. Feature-based methods only exploit limited feature points--typically image corners and blobs or line segments, such as classic MonoSLAM [17] and ORB-SLAM [18; 19]. They are not suitable for 3D semantic mapping due to rather sparse feature points. In order to better exploit image information and avoid the cost on calculation of features, direct dense SLAM system, such as the surfel-based dense slam, ElasticFusion [14], and Dense Visual SLAM [20], have been proposed recently. Whereas, direct image alignment from these dense methods is well-established for monocular, RGB-D and stereo sensors. Semi-dense methods like Large-Scale Direct-SLAM (LSD-SLAM) [21] and Semi-direct Visual Odometry (SVO) [22] provide possibility to build a synchronized 3D semantic mapping system. Deep CNNs have proven to be effective in the field of image semantic segmentation. [PERSON] et al. [23] firstly introduces an inverse convolution layer to realize an end-to-end training and inference. Then, the encoder-decoder architectures with specified upsampling layers, such as max unpooling and deconvolutional layer, are proposed to avoid the problem of separate step training in the Fully Convolutional Network (FCN) and improve the accuracy [15; 24]. [PERSON] et al. [4] exploit the capability of global context information through embedding various scenery context feature in a pyramid structure. The fusion of varied scaled feature has been a popular strategy in the recent deep CNNs. The cutting-edge method, namely, DeepLab series [5; 7], combines atrous convolutions and atrous spatial pyramid pooling (ASPP) to achieve a state-of-the-art performance on semantic segmentation. The early DeepLab models have a reasonable accuracy but require much computation overhead. Recently proposed efficient convolution neural network, such as MobileNets [25; 26] boosts real-time performance of semantic segmentation without losing the accuracy too much. The state-of-the-art DeepLab-v3+ [7] contains a simple effective decoder module to refine the segmentation results especially along object boundaries. Furthermore, combining the encoder part of MobileNet-v2 in its structure, DeepLab-v3+ could achieve a better trade-off between precision and runtime. In the topic of scene understanding and mapping, recent researchs employ 3D priors of objects increasingly. [PERSON] et al. [16] project 3D mesh of objects to the RGB-D frame in a graphical SLAM framework. [PERSON] et al. [27] propose a triangulated meshed representation of the scene from multiple depth measurements and exploit the CRF to capture the consistency of 3D object mesh. [PERSON] et al. [28] exploit the CRF for joint voxels to infer the semantic information and occupancy. [PERSON] and [PERSON] [29] use stereo camera, estimated pose, and CRF to infer the semantic octree presentation of the 3D scene. [PERSON] et al. [30] propose an incremental dense stereo reconstruction and semantic fusion technique to handle dynamic objects in the large-scale outdoor scenes. [PERSON] et al. [31] employ scene flow measurements to incorporate temporal updates into the mapping of dynamic environment. [PERSON] et al.