Khronos Group Welcomes 3dMD as Contributor Member.
The Khronos Group is creating interoperability standards for 3D, AR/VR, vision, and machine learning.
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis. X Liu, B Li, E Bron, W Niessen, E Wolvius, G Roshchupkin.
The proposed method was evaluated on the analysis of 3D facial shape and patient characteristics (N=5011). Experiments showed that this conceptually simple method achieved state-of-the-art fair prediction performance and interpretability, showing its great potential for clinical applications.
3D human tongue reconstruction from single “in-the-wild” images. S Ploumpis, S Moschoglou, V Triantafyllou, S Zafeiriou.
In this work, we presented the first pipeline which is able to perform 3D head and tongue reconstruction from a single image. To achieve this, we collected the first diverse tongue dataset with various tongue shapes and positions which we make publicly available to the research community.
Posture-Invariant 3D Human Hand Statistical Shape Model. Y Yang, T Yuan, T Huysmans, WS Elkhuizen, F Tajdari, Y Song.
It is concluded that the proposed posture correction approach can effectively improve the accuracy of the hand SSM and therefore enables its wide usage in human-integrated digital twin applications.
Anthropometric accuracy of three-dimensional average faces compared to conventional facial measurements. Z Shan, R TC Hsung, C Zhang et al.
This excerpt will show up on the home page Blog/News feed
Statistical Shape and Pose Model of the Forearm for Custom Splint Design. F Danckaers, JV Houtte, BG Booth, F Verstreken and J Sijbers.
This excerpt will show up on the home page Blog/News feed
Three-dimensional virtual planning in mandibular advancement surgery: soft tissue prediction based on deep learning. R ter Horst, H van Weert, T Loonen, S Bergé, S Vinayahalingam, F Baan, T Maal, G de Jong, T Xi.
This study focused on developing a novel deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery and comparing its accuracy with the mass tensor model (MTM).
Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods. X Li, J Liu, J Baron et al.
The workflow… we first performed facial geometry capture with a 3dMD system; imported into landmarker.io to annotate each face manually to achieve ground-truth; rasterized each face at 49 angles and 6 focal lengths; calculated the ground-truth 2D landmark locations; and analyzed performance of each method by calculating NRMSE error between a method’s predicted landmarks and the 2D ground-truth locations.
Disturbance-Region Removal on Landmark-less Registration of Craniofacial Images Acquired by CBCT and 3D Photography. Shu-Yen Wan, Lun-Jou Lo, Che-Yao Chang.
This excerpt will show up on the home page Blog/News feed
Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model. LJ Lo, CT Yang, CT Ho, CH Liao, HH Lin.
This study applied the transfer learning model with a convolutional neural network based on 3-dimensional (3D) contour line features to evaluate the facial symmetry before and after OGS. A total of 158 patients were recruited in a retrospective cohort study for the assessment and comparison of facial symmetry before and after OGS from January 2018 to March 2020. Three-dimensional facial photographs were captured by the 3dMD face system in a natural head position, with eyes looking forward, relaxed facial muscles, and habitual dental occlusion before and at least 6 months after surgery.