Balancing Runtime Space- and Time Complexity in Synthetic Database Driven Hand Posture Reconstruction Systems

Hand posture reconstruction systems based on large databases of synthetically rendered images of a 3D hand model offer a simple and flexible means of exploiting domain knowledge to provide training data. Such systems may also be applied to other domains in the posture reconstruction field by changing the model under consideration. Typically, the index structures used to answer similarity queries at runtime explicitly contain the prerendered feature data. However, the combinatorial explosion resulting from the multiple degrees of freedom available to the human hand severely limits the complexity of feature data that may be embedded into the index structure. The system presented in this paper exploits real-time objectspace rendering techniques to rebalance the preprocessing and runtime workloads such that the space complexity of the database relative to the number of degrees of freedom is greatly reduced. A prototype of the database subsystem is implemented and its properties investigated to obtain insight into its scaling behaviour.