Improving 3D Reconstruction for Digital Art Preservation

Achieving a high fidelity triangle mesh from 3D digital reconstructions is still a challenge, mainly due to the harmful effects of outliers in the range data. In this work, we discuss these artifacts and suggest improvements for two widely used volumetric integration techniques: VRIP and Consensus Surfaces (CS). A novel contribution is a hybrid approach, named IMAGO Volumetric Integration Algorithm (IVIA), which combines strengths from both VRIP and CS while adds new ideas that greatly improve the detection and elimination of artifacts. We show that IVIA leads to superior results when applied in different scenarios. In addition, IVIA cooperates with the hole filling process, improving the overall quality of the generated 3D models. We also compare IVIA to Poisson Surface Reconstruction, a state-of-the-art method with good reconstruction results and high performance both in terms of memory usage and processing time.

[1]  Yutaka Takase,et al.  The Great Buddha Project: Modelling Cultural Heritage through Observation , 2001 .

[2]  Paolo Cignoni,et al.  The Marching Intersections algorithm for merging range images , 2003, The Visual Computer.

[3]  Atsushi Nakazawa,et al.  The Great Buddha Project: Digitally Archiving, Restoring, and Analyzing Cultural Heritage Objects , 2007, International Journal of Computer Vision.

[4]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[5]  Holly E. Rushmeier,et al.  The 3D Model Acquisition Pipeline , 2002, Comput. Graph. Forum.

[6]  Randal C. Burns,et al.  Parallel Poisson Surface Reconstruction , 2009, ISVC.

[7]  Takeshi Masuda,et al.  Object shape modelling from multiple range images by matching signed distance fields , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[8]  Marc Levoy,et al.  Zippered polygon meshes from range images , 1994, SIGGRAPH.

[9]  Steve Marschner,et al.  Filling holes in complex surfaces using volumetric diffusion , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[10]  Randal C. Burns,et al.  Multilevel streaming for out-of-core surface reconstruction , 2007, Symposium on Geometry Processing.

[11]  Hans-Peter Seidel,et al.  Multi-level partition of unity implicits , 2005, SIGGRAPH Courses.

[12]  Katsushi Ikeuchi,et al.  Robust and adaptive integration of multiple range images with photometric attributes , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  William E. Lorensen,et al.  Marching cubes: a high resolution 3D surface construction algorithm , 1996 .

[14]  Daniel Cohen-Or,et al.  Competing Fronts for Coarse–to–Fine Surface Reconstruction , 2006, Comput. Graph. Forum.

[15]  Gabriel Taubin,et al.  The ball-pivoting algorithm for surface reconstruction , 1999, IEEE Transactions on Visualization and Computer Graphics.

[16]  Katsushi Ikeuchi,et al.  Taking consensus of signed distance field for complementing unobservable surface , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[17]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[18]  Marc Levoy,et al.  The digital Michelangelo project: 3D scanning of large statues , 2000, SIGGRAPH.

[19]  Roberto Cipolla,et al.  Computer Vision — ECCV '96 , 1996, Lecture Notes in Computer Science.

[20]  Herbert Edelsbrunner,et al.  Shape Reconstruction with Delaunay Complex , 1998, LATIN.

[21]  D. Cohen-Or,et al.  Robust moving least-squares fitting with sharp features , 2005, ACM Trans. Graph..

[22]  Luciano Silva,et al.  A 3D reconstruction pipeline for digital preservation , 2009, CVPR.

[23]  Adrian Hilton,et al.  Reliable Surface Reconstructiuon from Multiple Range Images , 1996, ECCV.

[24]  Katsushi Ikeuchi,et al.  Consensus surfaces for modeling 3D objects from multiple range images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).