Assisted color acquisition for 3D models

Abstract Capturing surface appearance precisely is paramount for modeling realistic materials. Nevertheless, the spatially varying nature of most materials is difficult to measure. State-of-the-art methods often rely on complex apparatus and controlled environments, and even if they are able to acquire reliable SVBRDFs, the whole process usually takes a long time and generates a large amount of data, that is often redundant. In this work, we propose a method for fast and assisted acquisition of material properties on-site. The system has a simple setup, requiring only a generic camera and a light source. Consequently, it is also very portable and appropriate for a broad range of object sizes and scenarios. The system guides the acquisition process, allowing for a fast capture session while at the same time producing high-quality per vertex diffuse colors. To help in achieving a complete coverage it suggests missing light directions, reducing the amount of necessary input images and the acquisition time. The system is designed to work in situ , therefore the whole acquisition process works with immediate feedback and interactive integration of new data. We show results for a variety of objects differing in size and materials.

[1]  Tim Weyrich,et al.  Principles of Appearance Acquisition and Representation , 2009, Found. Trends Comput. Graph. Vis..

[2]  Tim Weyrich,et al.  Principles of appearance acquisition and representation , 2007, SIGGRAPH '08.

[3]  Hans-Peter Seidel,et al.  Image-Based Reconstruction of Spatially Varying Materials , 2001 .

[4]  Roberto Scopigno,et al.  A Statistical Method for SVBRDF Approximation from Video Sequences in General Lighting Conditions , 2012, Comput. Graph. Forum.

[5]  Kun Zhou,et al.  AppFusion: Interactive Appearance Acquisition Using a Kinect Sensor , 2015, Comput. Graph. Forum.

[6]  Adrien Treuille,et al.  Example-Based Stereo with General BRDFs , 2004, ECCV.

[7]  Jaakko Lehtinen,et al.  Two-shot SVBRDF capture for stationary materials , 2015, ACM Trans. Graph..

[8]  David J. Kriegman,et al.  Photometric stereo with non-parametric and spatially-varying reflectance , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Beatriz Trinchao Andrade,et al.  Improving the Selection of Bases of BRDFs for Appearance Preservation , 2016, 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

[10]  Didier Stricker,et al.  Fully Automatic, Omnidirectional Acquisition of Geometry and Appearance in the Context of Cultural Heritage Preservation , 2015, ACM Journal on Computing and Cultural Heritage.

[11]  Julie Dorsey,et al.  Digital Modeling of Material Appearance , 2007 .

[12]  Thomas Malzbender,et al.  Polynomial texture maps , 2001, SIGGRAPH.

[13]  Szymon Rusinkiewicz,et al.  Principles and Practices of Robust, Photography-based Digital Imaging Techniques for Museums , 2010, VAST.

[14]  Steven M. Seitz,et al.  Shape and Spatially-Varying BRDFs from Photometric Stereo , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Christopher Schwartz,et al.  Integrated High-Quality Acquisition of Geometry and Appearance for Cultural Heritage , 2011, VAST.

[16]  Pieter Peers,et al.  Mobile Surface Reflectometry , 2016, Comput. Graph. Forum.

[17]  Pieter Peers,et al.  Appearance-from-motion , 2014, ACM Trans. Graph..

[18]  Daniel Cohen-Or,et al.  A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics, and Image Processing , 2015 .

[19]  Hans-Peter Seidel,et al.  Image-based reconstruction of spatial appearance and geometric detail , 2003, TOGS.

[20]  Christopher Schwartz,et al.  Data Driven Surface Reflectance from Sparse and Irregular Samples , 2012, Comput. Graph. Forum.

[21]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[22]  Jaakko Lehtinen,et al.  Practical SVBRDF capture in the frequency domain , 2013, ACM Trans. Graph..

[23]  Jannik Boll Nielsen,et al.  On optimal, minimal BRDF sampling for reflectance acquisition , 2015, ACM Trans. Graph..

[24]  Rafael Monroy,et al.  CultLab3D - On the Verge of 3D Mass Digitization , 2014, GCH.

[25]  Jason Lawrence,et al.  A coaxial optical scanner for synchronous acquisition of 3D geometry and surface reflectance , 2010, ACM Transactions on Graphics.

[26]  Ko Nishino,et al.  Automatically discovering local visual material attributes , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Baining Guo,et al.  Pocket reflectometry , 2011, SIGGRAPH 2011.

[28]  Katsushi Ikeuchi,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence Bi-polynomial Modeling of Low-frequency Reflectances , 2022 .

[29]  Paolo Cignoni,et al.  Flow-Based Local Optimization for Image-to-Geometry Projection , 2012, IEEE Transactions on Visualization and Computer Graphics.

[30]  Roberto Scopigno,et al.  Image‐to‐Geometry Registration: a Mutual Information Method exploiting Illumination‐related Geometric Properties , 2009, Comput. Graph. Forum.

[31]  Todd E. Zickler,et al.  A coaxial optical scanner for synchronous acquisition of 3D geometry and surface reflectance , 2010, SIGGRAPH 2010.