Table 5. Registration failure rates for registering a mesh target shape. (Experiment 2B).

Alg.	Failure Rate (%) by Test Case
	1	2	3	4	5	6	7	8	9
ICP	0.3	0.7	0.3	1.0	0.3	2.0	1.0	0.7	0.3
IMLP-CP	0.3	0.7	0.3	1.0	0.3	2.0	1.0	0.7	0.7
IMLP-MD	0.3	0.7	0.3	1.0	0.3	2.0	1.0	0.7	0.7
IMLP	0.3	0.7	0.3	1.0	0.3	2.0	1.0	0.7	0.7

Source shapes were randomly generated from a mesh model of a human hip (Fig. 1A), misaligned by [15, 30] mm / degrees in (Experiment 2A) and [30, 60] mm / degrees in (Experiment 2B), and registered directly back to the mesh. The test cases represent the different noise models used to generate noise on the source shape (Table 4). For each test case, 300 randomized trials were conducted, with the percent of unsuccessful registrations (TRE > 10 mm) being shown in this table. The proposed IMLP algorithm was evaluated relative to standard ICP [1] and relative to near-comparisons of GTLS-ICP [10] and A-ICP [12] using IMLP-CP and IMLP-MD, which modify IMLPâs most-likely match criteria to that of closest-point and Mahalanobis-distance matching, respectively. Failure rates for Experiment 2A (which are not shown in this table) were 0% for all algorithms and test cases.