Image-Driven Mesh Optimization

Peter Lindstrom, Greg Turk
Georgia Institute of Technology

Technical report GIT-GVU-00-16, June 2000.

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Abstract

We describe a method of improving the appearance of a low vertex count mesh in a manner that is guided by rendered images of the original, detailed mesh. This approach is motivated by the fact that greedy simplification methods often yield meshes that are poorer than what can be represented with a given number of vertices. Our approach relies on edge swaps and vertex teleports to alter the mesh connectivity, and uses the downhill simplex method to simultaneously improve vertex positions and surface attributes. Note that this is not a simplification method--the vertex count remains the same throughout the optimization. At all stages of the optimization the changes are guided by a metric that measures the differences between rendered versions of the original model and the low vertex count mesh. This method creates meshes that are geometrically faithful to the original model. Moreover, the method takes into account more subtle aspects of a model such as surface shading or whether cracks are visible between two interpenetrating parts of the model.


7a 7b 7c 7d 7e
7a.  Vertex
clustering
V = 769
7b.  Optimized
time = 1:49
E = 75.3%
7c.  Optimized
time = 11:36
E = 51.5%
7d.  Optimized
time = 1:02:14
E = 36.5%
7e.  Optimized
time = 6:00:19
E = 25.3%
7f 7g 7h 7k 7j
7f.  Memoryless
simplification
V = 341
7g.  Optimized
E = 59.7%
7h.  Original
model
V = 34,834
7i.  Memoryless
simplification
V = 693
7j.  Optimized
E = 58.8%
8a 8b 8c
8a.  Memoryless simplification
V = 4,095
8b.  Original model
V = 435,545
8c.  Optimized
E = 33.5%
9a 9b 9c
9a.  Memoryless simplification
V = 580
9b.  Original model
V = 33,173
9c.  Optimized
E = 51.0%
10a 10b
10a.  Original model
V = 24,070
10b.  Memoryless simplification
V = 333
10c 10d
10c.  Image-driven simplification
V = 309
10d.  Optimized
E = 60.1%