Large-scale 3D point clouds have been actively used in many applications with the advent of capturing devices. In this paper, we propose a novel saliency detection algorithm for large-scale colored 3D point clouds which capture real-world scenes. We first voxelize an input point cloud, and then partition voxels into a supervoxel which corresponds to a clusters at the lowest level. We construct the supervoxel cluster hierarchy iteratively, where a high level cluster includes low level clusters which exhibit similar features to each other. We also estimate the saliency at each cluster by computing the distinctness of geometric and color features based on center-surround contrast. By averaging the multiscale saliency maps obtained at different levels of clusters, we obtain final saliency distribution. Experimental results demonstrate that the proposed algorithm extracts globally and locally salient regions from large-scale colored 3D point clouds faithfully by employing the geometric and photometric features together.