My proposal was granted by the National Natural Science Foundation of China. 
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The basic information of the granted project is below (The proposal was written in Chinese):
Title:
Automatic identification and fine information extraction of atypical objects in the urban road environment.
Abstract:
Because of its fast data collection capability in form of dense 3D point cloud data, Mobile Laser Scanning (MLS) has been extensively applied in the efficient extraction of road information, object recognition, and digital map generation. However, there are various atypical objects in the urban road environments, which are unstructured, non-salient features and randomly distributed. This is the bottleneck problem that leads to the automatic recognition and fine information extraction of those objects being difficult, thus, restricting the application of MLS in digital city management. The existing methods mainly focus on the typical objects in the urban road environments, such as poles, signs, road markings, etc. What lacking are reliable methods for the automatic identification and fine information extraction of the atypical objects in the urban road environment, i.e. illegal parking, road furniture wearing, litterings, road-surface rainwater pondings, etc. The focus of this research is to drive intelligent methods for automatic identification and fine information extraction of those atypical objects based on point cloud and image data obtained by MLS. The main objectives consist of three parts: 1) Fine segmentation of urban road surface based on the dual grids of adaptive scales; 2) Reconstruction and fine segmentation of urban road surface using neighborhood tensor voting and bending energy optimization, and 3) Automatic identification and fine information extraction of atypical objects of interest in urban road environments. The output of this research will be to apply reliable technical support for digital and smart daily city management.
