Robust 3D object modeling with a low-cost RGBD-sensor and AR-markers for applications with untrained end-users

n approach for generating textured 3D models of objects without the need for complex infrastructure such as turn-tables or high-end sensors on precisely controlled rails is presented. The method is inexpensive as it uses only a low-cost RGBD sensor, e.g., Microsoft Kinect or ASUS Xtion, and Augmented Reality (AR) markers printed on paper sheets. The sensor can be moved by hand by an untrained person and the AR-markers can be arbitrarily placed in the scene, thus allowing the modeling of objects of a large range of sizes. Due to the use of the simple AR markers, the method is significantly more robust than just using the RGBD sensor or a monocular camera alone and it hence avoids the typical need for manual post-processing of alternative approaches like Kinect-Fusion, 123D Catch, Photosynth, or similar. This article has two main contributions: First, the development of a simple, inexpensive method for the quick and easy digitization of physical objects is presented. Second, the development of an uncertainty model for AR-marker pose estimation is introduced. The latter is of interest beyond the object modeling application presented here. The uncertainty model is used in a graph-based relaxation method to improve model-consistency. Realistic modeling of various objects, such as parcels, sport balls, coffee sacks, human dolls, etc., is experimentally demonstrated. Good model-accuracy is shown for several ground-truth objects with simple geometries and known dimensions. Furthermore, it is shown that the models obtained using the uncertainty model have fewer errors than the ones obtained without it. AR Markers used to create 3D models of common objects with RGBD sensors.An uncertainty model of AR-marker pose estimation is used to improve model consistency.Extensive experimental validation is done.