A 3D Model Compression Method for Large Scenes

Three-dimensional (3D) models are increasingly becoming an important tool in project management. At the same time, the 3D data of construction industry is more and more large, whether it is caused by large model scene or the requirement of model accuracy. In order to meet the requirements of the model application, the 3D model needs to be simplified. Mesh models are one of the most common ways to display 3D models, which provide well-defined object boundaries. The simplified algorithm of the existing mesh model cannot keep the important detail features of the original model once the data volume of the model is further reduced. Combined with Quadric Error Metrics (QEM) algorithm, this paper improves the edge collapse algorithm to solve the problem of undetected model structure and over-simplified model details. The proposed algorithm can preserve the integrity of the model while maintaining a high compression efficiency. Experiments show that the proposed method works well for the compression of large-scale scene models, which includes the compression ratio and the structural preservation in the model. This compression method is well suited for the model with huge amounts of data generated by large scene objects in the construction field.

[1]  Narciso García,et al.  Progressive Lower Trees of Wavelet Coefficients: Efficient Spatial and SNR Scalable Coding of 3D Models , 2005, PCM.

[2]  William E. Lorensen,et al.  Decimation of triangle meshes , 1992, SIGGRAPH.

[3]  Nadia Magnenat-Thalmann,et al.  A Highly Efficient Compression Framework for Time-Varying 3-D Facial Expressions , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Reinhard Klein,et al.  Real-time point cloud compression , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Michael Deering,et al.  Geometry compression , 1995, SIGGRAPH.

[6]  Mani Golparvar-Fard,et al.  Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques , 2011 .

[7]  Rémy Prost,et al.  Wavelet-based progressive compression scheme for triangle meshes: wavemesh , 2004, IEEE Transactions on Visualization and Computer Graphics.

[8]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[9]  Renato Pajarola,et al.  Compressed Progressive Meshes , 2000, IEEE Trans. Vis. Comput. Graph..

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

[11]  C.-C. Jay Kuo,et al.  Technologies for 3D mesh compression: A survey , 2005, J. Vis. Commun. Image Represent..

[12]  Greg Turk,et al.  Fast and memory efficient polygonal simplification , 1998 .

[13]  Yuan Huang,et al.  Compression algorithm of scattered point cloud based on octree coding , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[14]  Markus H. Gross,et al.  Spectral processing of point-sampled geometry , 2001, SIGGRAPH.

[15]  Dietmar Saupe,et al.  Image‐Based Surface Compression , 2008, Comput. Graph. Forum.

[16]  Reinhard Klein,et al.  A Parallelly Decodeable Compression Scheme for Efficient Point-Cloud Rendering , 2007, PBG@Eurographics.

[17]  Mike M. Chow Optimized geometry compression for real-time rendering , 1997 .

[18]  Bo Zhou,et al.  Multi-Feature Metric-Guided Mesh Simplification , 2014 .

[19]  Shyi-Chyi Cheng,et al.  A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis , 2010, Pattern Recognit..

[20]  Andreas Nüchter,et al.  One billion points in the cloud – an octree for efficient processing of 3D laser scans , 2013 .

[21]  Peter Lindstrom,et al.  Out-of-core simplification of large polygonal models , 2000, SIGGRAPH.

[22]  Diego Viejo,et al.  3DCOMET: 3D compression methods test dataset , 2016, Robotics Auton. Syst..

[23]  H. Aoyama,et al.  Mesh Simplification Based on Feature Preservation and Distortion Avoidance for High-Quality Subdivision Surfaces , 2013 .

[24]  Reinhard Klein,et al.  Fast vector quantization for efficient rendering of compressed point-clouds , 2008, Comput. Graph..

[25]  Sang Won Bae,et al.  3D medial axis point approximation using nearest neighbors and the normal field , 2011, The Visual Computer.

[26]  David Lattanzi,et al.  3D Scene Reconstruction for Robotic Bridge Inspection , 2015 .

[27]  Jarek Rossignac,et al.  Grouper: A Compact, Streamable Triangle Mesh Data Structure. , 2013, IEEE transactions on visualization and computer graphics.

[28]  Billie F. Spencer,et al.  Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction , 2016, J. Comput. Civ. Eng..

[29]  Yuh-Dauh Lyuu,et al.  Linear-time compression of 2-manifold polygon meshes into information-theoretically optimal number of bits , 2011, Appl. Math. Comput..

[30]  Yo-Sung Ho,et al.  Predictive compression of geometry, color and normal data of 3-D mesh models , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  Ali Khaloo,et al.  Hierarchical Dense Structure-from-Motion Reconstructions for Infrastructure Condition Assessment , 2017, J. Comput. Civ. Eng..

[32]  Hyojoo Son,et al.  3D structural component recognition and modeling method using color and 3D data for construction progress monitoring , 2010 .