Map-based localization and sensing are one of the key components in autonomous driving technologies, where high quality 3D map reconstruction is fundamentally utmost important. However, due to the highly dynamic and uncontrollable properties of real world environment, building a high quality 3D map is not straightforward and requires several strong assumptions. To address this challenge, we present a complete framework, which detects and extracts the moving objects from a sequence of unordered and texture-less point clouds, to build high quality static maps. To accurately detect the moving objects from data acquired with a possibly fast moving platform, we propose a novel 3D Flow Field Analysis approach in which we inspect the motion behaviour of the registered point sets. The proposed algorithm elegantly models the temporal and spatial displacement of the moving objects. Thus, both small moving objects (e.g. walking pedestrians) and large moving objects (e.g. moving trucks) can be detected effectively. Further, by incorporating the Sparse Subspace Clustering framework, we propose a Sparse Flow Clustering algorithm to group the 3D motion flows under both the constraints of motion similarity and spatial closeness. To this end, the static scene parts and the moving objects can be independently processed to achieve photo-realistic 3D reconstructions. Finally, we show that the proposed 3D Flow Field Analysis algorithm and the Sparse Flow Clustering approach are highly effective for motion detection and segmentation, as exemplified on the KITTI benchmark, and yield high quality reconstructed static-maps as well as rigidly moving objects.