Advanced SearchSearch Tips
3D Head Modeling using Depth Sensor
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
3D Head Modeling using Depth Sensor
Song, Eungyeol; Choi, Jaesung; Jeon, Taejae; Lee, Sangyoun;
  PDF(new window)
Purpose We conducted a study on the reconstruction of the head`s shape in 3D using the ToF depth sensor. A time-of-flight camera (ToF camera) is a range imaging camera system that resolves distance based on the known speed of light, measuring the time-of-flight of a light signal between the camera and the subject for each point of the image. The above method is the safest way of measuring the head shape of plagiocephaly patients in 3D. The texture, appearance and size of the head were reconstructed from the measured data and we used the SDF method for a precise reconstruction. Materials and Methods To generate a precise model, mesh was generated by using Marching cube and SDF. Results The ground truth was determined by measuring 10 people of experiment participants for 3 times repetitively and the created 3D model of the same part from this experiment was measured as well. Measurement of actual head circumference and the reconstructed model were made according to the layer 3 standard and measurement errors were also calculated. As a result, we were able to gain exact results with an average error of 0.9 cm, standard deviation of 0.9, min: 0.2 and max: 1.4. Conclusion The suggested method was able to complete the 3D model by minimizing errors. This model is very effective in terms of quantitative and objective evaluation. However, measurement range somewhat lacks 3D information for the manufacture of protective helmets, as measurements were made according to the layer 3 standard. As a result, measurement range will need to be widened to facilitate production of more precise and perfectively protective helmets by conducting scans on all head circumferences in the future.
Modeling;ToF sensor;Medical image processing;Surface Rendering;Positional plagiocephaly;
 Cited by
Curless B, Levoy M. A volumetric method for building complex models from range images. In SIGGRAPH, 1996

Kubacki DB, Bui HQ, Babacan SD, Do MN. Registration and integration of multiple depth images using signed distance function. In SPIE, Computational Imaging X, 2012

Canelhas D. Scene representation, registration and object detection in a truncated signed distance function representation of 3d space. Master's thesis, 2012. Solution

Bylow E, Sturm J, Kerl C, Kahl F, Cremers D. Real-time camera tracking and 3d reconstruction using signed distance functions. In RSS, 2013

Schaaf H, Malik CY, Streckbein P, Pons-Kuehnemann J, Howaldt HP, Wilbrand JF. Three-dimensional photographic analysis of outcome after helmet treatment of a nonsynostotic cranial deformity. J Craniofac Surg 2010;21:1677-1682 crossref(new window)

Kim SY. "Outcome Analysis of Helmet Therapy and Counter Positioning for Deformational Plagiocephaly", 2013.,Thesis(M.A.)--Ajou University

Kang JW, Kim DY, Lee SH. "Segmentation and 3D Visualzation of Medical Image: An Overview", 2014. Journal of International Society for Simulation Surgery 2014;(1):27-31

Ma Y, Soatto S, Kosecka J, Sastry S. An Invitation to 3D Vision: From Images to Geometric Models. Springer Verlag, 2003

Newcombe RA, Izadi S, Hilliges O, MolyneauxD, Kim D, Davison AJ, Kohli P, Shotton J, Hodges S, Fitzgibbon AW. KinectFusion: Real-time dense surface mapping and tracking. In ISMAR 2011;127-136

Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal Mach Intell 1992;14(2):239-256 crossref(new window)

Whelan T, Johannsson H, Kaess M, Leonard JJ, McDonald JB. Robust real-time visual odometry for dense RGB-D mapping. In IEEE Intl. Conf. on Robotics and Automation, ICRA, Karlsruhe, Germany, May 2013