Human Body Volume Recovery from Single Depth Image

We propose on-line human body volume recovery framework using only single depth image. Depth image contains partial 3d geometry information of human body surface seen from the sensor viewpoint. Previous volume reconstruction methods require multiple images from different viewpoints to reconstruct complete closed body volume. They have limitation in real-time application with dynamic objects or require multiple sensors. In this paper, we propose a generic model based human body volume recovery. First, we register the pose of the generic model to partial body surface observation from single depth image. And then remaining 3d points on unseen surface is optimized by propagating confidence of the partial observation. Experimental result shows our method captures reasonable human body volume from single depth image on-line.

[1]  Martin Klaudiny,et al.  Single-View RGBD-Based Reconstruction of Dynamic Human Geometry , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[2]  Ming Zeng,et al.  Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Angelos Barmpoutis,et al.  Tensor Body: Real-Time Reconstruction of the Human Body and Avatar Synthesis From RGB-D , 2013, IEEE Transactions on Cybernetics.

[4]  Andrew W. Fitzgibbon,et al.  Real-time non-rigid reconstruction using an RGB-D camera , 2014, ACM Trans. Graph..

[5]  Charlie C. L. Wang,et al.  Volumetric template fitting for human body reconstruction from incomplete data , 2014 .

[6]  Alla Sheffer,et al.  Template-based mesh completion , 2005, SGP '05.

[7]  Seungkyu Lee Time-of-flight depth camera accuracy enhancement , 2012 .

[8]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[9]  Zicheng Liu,et al.  Tensor-Based Human Body Modeling , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Bo Fu,et al.  Quality Dynamic Human Body Modeling Using a Single Low-Cost Depth Camera , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.