A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
ACM Transactions on Graphics (TOG)
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
A Geometric Approach to 3D Object Comparison
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Shape-Based 3D Model Retrieval
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
ACM SIGGRAPH 2004 Papers
Symmetry descriptors and 3D shape matching
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
An Approximate and Efficient Method for Optimal Rotation Alignment of 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model Composition from Interchangeable Components
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
A GPU based high-efficient and accurate optimal pose alignment approach of 3D objects
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
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In this paper, we address the challenging task of finding the best alignment between two 3D objects by solving a global optimization problem in the space of rotations SO(3). The objective function to be optimized is a newly developed rotation-variant similarity measure, which is obtained directly from the object's geometry and is entirely implemented on the GPU. By exploiting the modern GPU's parallel architecture, we can process considerably greater amounts of data than a CPU implementation can do in the same amount of time. This allows us to create a similarity measure which combines speed and accuracy. The actual problem of rotation alignment is then solved by finding the global maximum of this similarity function in the space of rotations. A special rotation representation allows for an efficient local optimization on the manifold SO(3). Furthermore, unwanted local maxima can be avoided by a heuristic global optimization procedure which exploits rotational symmetry. Due to this common sense heuristics, the global search can be gradually reduced to a lower-dimensional problem up to a 1D line search to handle objects with high rotational symmetry. We show that our method is superior to existing normalization techniques such as PCA and provides a high degree of precision despite remarkably short runtimes.