Reconstruction of Relatively Straight Medium to Long Hair Models using Kinect Sensors
Sun Chao, Zhongrui Li, Won‐Sook Lee · 2015
Most existing hair capturing methods reconstruct 3D hair models from multi-view stereo based on complex capturing systems composed of many digital cameras and light sources. In this paper, we introduce a novel hair capturing system using consumer RGB-D (Kinect sensors). Our capture system, consisting of three Kinect v2 sensors, is much simpler than previous hair capturing systems. We directly use the 3D point clouds captured by Kinect v2 sensors as the hair volume. Then we adopt a fast and robust image enhancement algorithm to adaptively improve the clarity of the hair strands geometry based on the estimated local strands orientation and frequency from the hair images captured by the Kinect colour sensors. In addition, we introduced a hair strand grow-and-connect algorithm to generate relatively complete hair strands. Furthermore, by projecting the 2D hair strands onto the 3D point clouds, we can obtain the corresponding 3D hair strands. The experimental results indicate that our method can generate plausible 3D models for long, relatively straight hair.