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Recently, surgical-orthodontic treatment without preoperative orthodontic treatment (known as the surgery-first approach or SFA) has been proposed to improve facial aesthetics from the beginning of treatment, to shorten the entire treatment period, and to take advantage of the regional accelerated phenomenon for orthodontic tooth movement. The SFA concept involves the prediction and simulation of dental alignment, incisor decompensation, and arch coordination using manual setup models. Based on this, decisions regarding the surgical movement of the maxilla and mandible can be made to correct skeletal discrepancies through manual model surgery (MMS). Although several three-dimensional (3D) virtual model surgery (VMS) programs have been introduced to reduce time-consuming manual laboratory procedures and potential errors, these programs still require three-dimensional-computed tomography data and involve complex computerized maneuvers. Because it is difficult to acquire 3D-computed tomographic scans in all cases, a 2.5-dimensional VMS program using two-dimensional lateral and posteroanterior cephalograms and 3D virtual dental models (3Txer version 2.5; Orapix, Seoul, Korea) has been introduced. In addition, because accurate prediction of postoperative orthodontic treatment is crucial for controlling dental alignment, incisor decompensation, arch coordination, and occlusal settling, a new 3D virtual orthodontic treatment system that can construct 3D virtual models, execute a 3D virtual setup, place virtual brackets at predetermined positions, and fabricate transfer jigs with customized bracket base for indirect bonding using a stereolithographic technique has also been developed. The purpose of this article was to introduce the methodology of 2.5-dimensional VMS and 3D virtual postoperative orthodontic treatment using a stereolithographic technique in SFA for a class III open-bite case.

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This study proposes a new pallet recognition system using Kinect camera. Depth image of Kinect camera is produced from the infrared ray data of random dot type. This system was applied to an automated guided vehicle(AGV) to recognize the pallet in various conditions. A modularized hardware and software of the pallet recognition system was developed. The performance of the developed pallet recognition system was tested through experiments under various environment, and it show good performance.

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