3D human tongue reconstruction from single “in-the-wild” images. S Ploumpis, S Moschoglou, V Triantafyllou, S Zafeiriou.
Imperial College London and Huawei build a 3D tongue reconstruction model with 3dMD’s real-world Science Museum, London dataset.
Khronos Group Welcomes 3dMD as Contributor Member.
The Khronos Group is creating interoperability standards for 3D, AR/VR, vision, and machine learning.
Identity-Disentangled Neural Deformation Model for Dynamic Meshes. B Xu, L Ma, Y Ye, T Schmidt, CD Twigg, S Lovegrove.
Meta Reality Labs and Imperial College London build a neural hand deformation model with 3dMD’s real-world 3DH scan data, 2021.
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.
Anthropometric accuracy of three-dimensional average faces compared to conventional facial measurements. Z Shan, R TC Hsung, C Zhang et al.
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Statistical Shape and Pose Model of the Forearm for Custom Splint Design. F Danckaers, JV Houtte, BG Booth, F Verstreken and J Sijbers.
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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.
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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.