Selectively-densified mesh construction for virtual environments using salient points derived from a computational model of visual attention

A possible solution to ensure real-time interaction with virtual environments, while not visibly degrading the quality of object models is to construct selectively-densified meshes, that preserve a higher density around the regions that characterize the most the object's shape and properties. The purpose of such an approach is to aim at improving the perceived quality of the models in those areas subjected to increased observation by users. In this paper, a classical computational visual attention model is employed on images collected from multiple viewpoints over the surface of an object to identify regions that attract visual attention. A novel approach is then proposed to allow the use of this model for the detection of salient points on the surface of 3D objects, including: an iterative technique to extract salient points from the saliency map, a procedure for the selection of viewpoints for saliency computation based on the best viewpoint for an object, and a projection algorithm to find the coordinates of the identified salient points in images on the surface of the 3D object. The areas around the identified salient points are constrained at maximum resolution in a selectively-densified mesh obtained using the QSlim simplification algorithm. The results are compared with existing solutions from the literature to demonstrate the superiority of the proposed approach.

[1]  Michael Garland,et al.  User-guided simplification , 2003, I3D '03.

[2]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[3]  Benjamin Bustos,et al.  A Robust 3D Interest Points Detector Based on Harris Operator , 2010, 3DOR@Eurographics.

[4]  Afzal Godil,et al.  Salient local 3D features for 3D shape retrieval , 2011, Electronic Imaging.

[5]  David W. Jacobs,et al.  Mesh saliency , 2005, SIGGRAPH 2005.

[6]  Carol O'Sullivan,et al.  An experimental approach to predicting saliency for simplified polygonal models , 2004, APGV '04.

[7]  J. Mixter Fast , 2012 .

[8]  Ralph R. Martin,et al.  Conditional random field-based mesh saliency , 2012, 2012 19th IEEE International Conference on Image Processing.

[9]  Dieter Schmalstieg,et al.  User-controlled creation of multiresolution meshes , 2003, I3D '03.

[10]  Afzal Godil,et al.  Evaluation of 3D interest point detection techniques via human-generated ground truth , 2012, The Visual Computer.

[11]  Tomas Akenine-Möller,et al.  Fast, Minimum Storage Ray-Triangle Intersection , 1997, J. Graphics, GPU, & Game Tools.

[12]  Ana-Maria Cretu,et al.  Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments , 2016 .

[13]  Christof Koch,et al.  Feature combination strategies for saliency-based visual attention systems , 2001, J. Electronic Imaging.

[14]  Paolo Cignoni,et al.  Metro: Measuring Error on Simplified Surfaces , 1998, Comput. Graph. Forum.

[15]  Leonidas J. Guibas,et al.  A concise and provably informative multi-scale signature based on heat diffusion , 2009 .

[16]  Umberto Castellani,et al.  Sparse points matching by combining 3D mesh saliency with statistical descriptors , 2008, Comput. Graph. Forum.

[17]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[18]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Valero Laparra,et al.  Perceptual image quality assessment using a normalized Laplacian pyramid , 2016, HVEI.

[20]  Afzal Godil,et al.  A benchmark for best view selection of 3D objects , 2010, 3DOR '10.

[21]  Ko Nishino,et al.  Scale-Dependent 3D Geometric Features , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  Chi-Han Peng,et al.  User-assisted mesh simplification , 2006, VRCIA '06.

[23]  Yong Huang,et al.  Texture decomposition by harmonics extraction from higher order statistics , 2004, IEEE Trans. Image Process..