## 1 Introduction  
Over the recent years, significant research effort has been spent to introduce the third dimension to computer imaging. In customer electronics, these efforts have already led to creation of new products, new markets and opportunities: gesture control is becoming a \"must have\" feature of forthcoming home entertainment systems, revealing the new concepts of _human-to-machine interfaces_ (HMI) [1; 2; 3]. For the industry, medicine, security and even archeology 3D vision systems are already there: surface quality control, non-invasive reconstruction of cultural heritage, reverse engineering, surveillance and face recognition systems--all of these applications utilize _3D imaging sensors_ or _range sensors_[4].  
As well as conventional cameras, range sensors are equipped with a light-sensitive element--either a standard or a specially designed CMOS or CCD sensor [5, 6, 7], but whereas classical cameras produce images or _bitmaps_ representing the light intensity distribution over the scene, this class of devices produces _range maps_--a digital representation of the scene surface based on information about the distances from the sensor to the objects of the scene.  
Several techniques have been proposed for range measurement [8, 9, 10, 11, 12, 13, 14, 15, 16]. They may be categorized in two main categories: _active_ and _passive_. Passive methods, such as _stereo-vision_[17] and _shape-from-focus_[18], do not use any special source of electromagnetic radiation for extracting the range information, while active range sensors illuminate the scene with some type of structured light in visible or infra-red spectrum with further reconstruction of 3D structure of the scene by measuring the temporal or spatial distribution of the reflected signal (_time-of-flight_[19], _interferometry_[20]).  
Miniaturization and increasing performance of CPU-s, plenty of cost-efficient and powerful _systems-on-chip_ (SoC-s) and embedded platforms, intensive evolution of CMOS sensors make forthcoming computer vision systems promising for solving a variety of _simultaneous localization and mapping_ (SLAM)-related tasks for robotics and autonomous vehicles. But there are still some challenges which should be solved before we can rely on these systems in real world environment. For outdoor scenarios, ambient light and attenuation of the transmitted signal due to the atmospheric effects cause problems in most of the existing techniques of range measurement [21]. Furthermore, in case when several active sensors scan the same area simultaneously, they may experience _mutual interference_ problems.  
In this paper we propose the solution which solves these problems--_modulated acquisition of projected patterns_. Reflected from the scene as a _spatial distortion map_, such patterns are utilized as robust raw data for further 3D reconstruction of the scene in real time. The discussed solution does not represent another pattern-based 3D scanning method; instead we are focusing on the robustness of data acquisition in order to improve the input for numerous existing range measurement approaches. The desired robustness is not achieved by any kind of spatial encoding like pattern, shape or color as suggested in some of the previous studies [22, 23, 24], but by signal encoding in the time domain.  
The paper is organized as follows: in Sections 2 and 3 the physical principle and the concept of the proposed solution are discussed. The outline of the design is given in Section 4. In Sections 5 and 6 the proposed performance evaluation methodology is discussed. In Section 7 the proof of feasibility is demonstrated for the proposed approach. Finally, the concluding remarks and further work are given in Section 8.