Yesterday: After Friday's struggle differentiating the maximal clique algorithm from my approximation approach, I determined that there doesn't appear to be much difference unless its application-specific. Have started implementing/adapting the maximal clique algorithm to speed up based on the conditions for 3D rigid object detection. Cursory results seem promising, though a wider selection of test cases needs to be explored.
Yesterday was spent getting Perera's implementation up and running (as it tends to). C-based implementation for maximal clique detection seems fast, but the datasets are small (and correspondences pre-determined). Requires further evaluation. Emailed Faramarz about discussion/direction.
Today: Start comparing and contrasting the maximal clique approach: re-arrange personal datasets for input into Perera's and similarly Perera's datasets into my own. Compare/time differences in maximal clique approaches/short-circuiting. Discuss/talk out with Faramarz how the approximate clique might be used for speedup.
Roadblocks: N/A
Where Does this Fit In: Good to get back at development, especially when the theoretical results don't seem adequate. Also require more extensive experimental data to test against, so good to collect.