A 3D modeling technique for an urban environment can be applied to several applications such as landscape simulations, navigational systems, and mixed reality systems. In this field, the target environment is first measured using several types of sensors (laser rangefinders, cameras, GPS sensors, and gyroscopes). A 3D model of the environment is then constructed based on the results of the 3D measurements. In this 3D modeling process, 3D points that exist on moving objects become obstacles or outliers to enable the construction of an accurate 3D model. To solve this problem, we propose a method for detecting 3D points on moving objects from 3D point cloud data based on photometric consistency and knowledge of the road environment. In our method, 3D points on moving objects are detected based on luminance variations obtained by projecting 3D points onto omnidirectional images. After detecting 3D the points based on evaluation value, the points are detected using prior information of the road environment. Index Terms-Outdoor Environment, 3D Modeling, 3D Point Cloud Data, Moving Objects, HSV Model

3D models of urban environments are constructed based on measured data using several types of sensors such as laser rangefinders, cameras, GPS sensors and gyroscopes. In this 3D modeling process, 3D points on moving objects become obstacles to enable the construction of an accurate 3D model. To solve this problem, this paper has proposed a method for detecting and removing 3D points on moving objects from 3D point cloud data based on photometric consistency and knowledge of the road environment. In our method, 3D points on moving objects are detected based on luminance variations computed by projecting 3D points onto omnidirectional images. After detecting candidates for points on moving objects, points inside a certain size of region determined by prior knowledge of road environment are removed. We show the effectiveness of the method through experiments for a real outdoor environment.