An efficient method for fully automatic 3D digitization of unknown objects

Abstract

Our goal is to develop a complete and automatic scanning strategy with minimum prior information about the object shape. We aim to establish a methodology for the automation of the 3D digitization process. The paper presents a novel approach to determine the Next Best View (NBV) for an efficient reconstruction of highly accurate 3D models. Our method is based on the classification of the acquired surfaces into Well Visible and Barely Visible combined with a best view selection algorithm based on mean shift, which avoids unreachable positions. Our approach is applicable to all kinds of range sensors. To prove the efficiency and the robustness of our method, test objects are first scanned manually by experts in 3D digitization from the VECTEO company. The comparison of results between manual and automatic scanning shows that our method is very efficient and faster than trained experts. The 3D models of the different objects are obtained with a strongly reduced number of acquisitions while moving efficiently the ranging device. The obtained results prove the effectiveness and the versatility of our 3D reconstruction approach for industrial applications.

Introduction

The 3D models of objects are widely used for an increasing number of applications such as industrial applications, entertainment, preservation of important cultural heritage artifacts, and architectural applications. In industrial applications, objects are digitized for inspection tasks, reverse engineering, and replication. Such applications demand high quality and accurate 3D models. The manual 3D digitization process is expensive since it requires a highly trained technician who decides about the different views needed to acquire the object model. The quality of the final result strongly depends, in addition to the complexity of the object shape, on the selected viewpoints and thus on the human expertise. Nowadays, the most developed digitization strategies in industry are based on a teaching approach in which a human operator manually determines one set of poses for the ranging device. The main drawback of this methodology is the influence of the operator's expertise. Moreover, this technique does not fulfill the high level requirement of industrial applications which require reliable, repeatable, and fast programming routines. Therefore, it is necessary to develop an efficient automatic digitization strategy while minimizing the impact of the human factor. In contrast to the manual and the teaching-based scanning, the automatic scanning deploys the most beneficial views for the reconstruction process to achieve the highest possible accuracy and coverage rate with the smallest number of views. Automatic scanning can be decomposed into two phases: the determination of the different poses (position and orientation) for the set sensor-object and the sensor trajectory generation from previous poses to achieve the next optimal location. The automation problem becomes a view planning problem in which one seeks for the Next Best Views (NBVs) to improve an existing model reconstruction and to cover all the object surface. The non-model based NBV planning can be seen as an incremental approach to building object or scene models using all the previously acquired 3D data.

The goal of this work is to automatically generate a complete 3D model of unknown and complex objects by developing an information-driven approach. Therefore, we introduce a novel NBV strategy based on the evolution of the scanned part orientation. Our method enables fast and complete 3D reconstruction while moving efficiently the scanner. By generating a set of potential views, our technique ensures proper avoidance of unreachable configurations. This paper is organized as follows. In the next section we discuss the related work and briefly define the adopted approach for our view planning strategy. In Section 3 we introduce our NBV planning method followed by experimental results in Section 4. We conclude and give an overview of the work in progress in Section 5.