Orientation field Depth map Final merged result PMVS + Poisson [4, 7] Figure 1: We begin with many high-resolution photographs (with unconstrained lighting), compute an orientation field for each, and perform multi-view stereo matching using a metric based on orientation similarity. The resulting depth maps show high-resolution details of hair strands and we integrate them into a single merged model. In contrast, conventional multi-view stereo algorithms and merging techniques [4, 7] fail at capturing the fine hair structures. Reconstructing realistic 3D hair geometry is challenging due to omnipresent occlusions, complex discontinuities and specular appearance. To address these challenges, we propose a multi-view hair reconstruction algorithm based on orientation fields with structure-aware aggregation. Our key insight is that while hair's color appearance is viewdependent, the response to oriented filters that captures the local hair orientation is more stable. We apply the structure-aware aggregation to the MRF matching energy to enforce the structural continuities implied from the local hair orientations. Multiple depth maps from the MRF optimization are then fused into a globally consistent hair geometry with a template refinement procedure. Compared to the state-of-the-art color-based methods, our method faithfully reconstructs detailed hair structures. We demonstrate the results for a number of hair styles, ranging from straight to curly, and show that our framework is suitable for capturing hair in motion.
REF proposed a method to grow a strand model in the orientation field constrained by the geometry constructed from hair orientation fields.
Multi-view hair capture using orientation fields
2012 IEEE Conference on Computer Vision and Pattern Recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science