AstroGen – Procedural Generation of Highly Detailed Asteroid Models

We present a novel algorithm, called AstroGen, to procedurally generate highly detailed and realistic 3D meshes of small celestial bodies automatically. AstroGen gains it's realism from learning surface details from real world asteroid data. We use a sphere packing-based metaball approach to represent the rough shape and a set of noise functions for the surface details. The main idea is to apply an optimization algorithm to adopt these representations to available highly detailed asteroid models with respect to a similarity measure. Our results show that our approach is able to generate a wide variety of different celestial bodies with very complex surface structures like caves and craters.

[1]  James M. Rehg,et al.  Terrain Synthesis from Digital Elevation Models , 2007, IEEE Transactions on Visualization and Computer Graphics.

[2]  Gabriel Zachmann,et al.  Invariant local shape descriptors: classification of large-scale shapes with local dissimilarities , 2017, CGI.

[3]  Brian Wyvill,et al.  Extending the CSG Tree. Warping, Blending and Boolean Operations in an Implicit Surface Modeling System , 1999, Comput. Graph. Forum.

[4]  Julian Togelius,et al.  Towards multiobjective procedural map generation , 2010, PCGames@FDG.

[5]  Swagatam Das,et al.  A closed loop stability analysis and parameter selection of the Particle Swarm Optimization dynamics for faster convergence , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  Mohamed S. Ebeida,et al.  Efficient and good Delaunay meshes from random points , 2011, Comput. Aided Des..

[7]  Ken Perlin,et al.  Improving noise , 2002, SIGGRAPH.

[8]  Mikko Kaasalainen,et al.  DAMIT: a database of asteroid models , 2010 .

[9]  S. Debei,et al.  Images of Asteroid 21 Lutetia: A Remnant Planetesimal from the Early Solar System , 2011, Science.

[10]  Eric Galin,et al.  Sparse representation of terrains for procedural modeling , 2016, Comput. Graph. Forum.

[11]  Ares Lagae,et al.  A Survey of Procedural Noise Functions , 2010, Comput. Graph. Forum.

[12]  Bedrich Benes,et al.  Terrain generation using procedural models based on hydrology , 2013, ACM Trans. Graph..

[13]  H. Miller Tobler's First Law and Spatial Analysis , 2004 .

[14]  Ian Parberry Designer Worlds: Procedural Generation of Infinite Terrain from Real-World Elevation Data , 2014 .

[15]  James F. Blinn,et al.  A generalization of algebraic surface drawing , 1982, SIGGRAPH.

[16]  C. T. Russell,et al.  Differentiation of the asteroid Ceres as revealed by its shape , 2005, Nature.

[17]  Bedrich Benes,et al.  Terrain Modelling from Feature Primitives , 2015, Comput. Graph. Forum.

[18]  Ares Lagae,et al.  State of the Art in Procedural Noise Functions , 2010, Eurographics.

[19]  Ken Perlin,et al.  [Computer Graphics]: Three-Dimensional Graphics and Realism , 2022 .

[20]  Rafael Bidarra,et al.  A Survey on Procedural Modelling for Virtual Worlds , 2014, Comput. Graph. Forum.

[21]  Gabriel Zachmann,et al.  Multi agent system optimization in virtual vehicle testbeds , 2015, SimuTools.

[22]  David S. Ebert,et al.  Texturing and modeling - a procedural approach, Third Edition , 2002, Morgan Kaufmann series in computer graphics and geometric modeling.

[23]  Steven Worley,et al.  A cellular texture basis function , 1996, SIGGRAPH.

[24]  Scott Schaefer,et al.  Dual marching cubes: primal contouring of dual grids , 2004, 12th Pacific Conference on Computer Graphics and Applications, 2004. PG 2004. Proceedings..

[25]  Rene Weller,et al.  Fast sphere packings with adaptive grids on the gpu , 2013 .

[26]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.