### Table 2: The relative errors in depth computation using our invariant algorithm, for a ne and rigid shape. We compared in Table 3 the average relative error of the results of our algorithm to the average relative error of a random set of 3D points, aligned to the ground truth data with the optimal similarity or a ne transformation. Rigid Invar. Rigid random A . Invar A . random 8:4% 27:6% 2:9% 23:3%

1995

"... In PAGE 18: ... similar sequence in [25] Fig. 4, or [15] Fig. 3.) Table2 summarizes the results of our invariant algorithm for the last 8 points. Due to the noise in the data and the large perspective distortions, not all the frames were consistent with rigid motion.... ..."

Cited by 56

### Table 1: Average distance to the closest points to the matched model after scan registration. The decrease of this distance measures the improvement of the model through local surface deformations.

2003

"... In PAGE 5: ... The standard registration will lead to multiple feet, our approach correctly aligns them. Table1 shows the cumulative distance between points in the nearest neighbor calculation. The value marked as start is the result of an initial registration phase, reflecting the remaining distances under the rigid body as- sumption.... ..."

Cited by 15

### Table 1: Average distance to the closest points to the matched model after scan registration. The decrease of this distance measures the improvement of the model through local surface deformations.

"... In PAGE 5: ... The standard registration will lead to multiple feet, our approach correctly aligns them. Table1 shows the cumulative distance between points in the nearest neighbor calculation. The value marked as start is the result of an initial registration phase, reflecting the remaining distances under the rigid body as- sumption.... ..."

### Table 2: Evaluation of matching algorithm for pattern recognition

"... In PAGE 13: ... Notice that the high threshold for the collective probability of match implies high accuracy of rules triggered for each particular time point in the pattern. Let us now describe the results of the experiments presented in Table2 . The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared.... In PAGE 13: ... The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared. The pattern number stated in Table2 represents the ordering number of patterns from Figure 4. The ordering of patterns is from left to right and then from the rst to the fourth line; the pattern number 1 is in the upper left corner and the pattern number 24 is in the lower right corner.... ..."

### Table 2: Evaluation of matching algorithm for pattern recognition

"... In PAGE 15: ... Notice that the high threshold for the collective probability of match implies high accuracy of rules triggered for each particular time point in the pattern. Let us now describe the results of the experiments presented in Table2 . The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared.... In PAGE 15: ... The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared. The pattern number stated in Table2 represents the ordering number of patterns from Figure 4. The ordering of patterns is from left to right and then from the rst to the fourth line; the pattern number 1 is in the upper left corner and the pattern number 24 is in the lower right corner.... ..."

### Table 2. Pattern Matching Costs.

"... In PAGE 13: ...Table2... In PAGE 13: ... Node a68 a1 passes on the tokens with the correct number of arguments and a68a128a133 creates an instantiation of the query and adds it to the conflict set. Performing the same analysis as before, the results are presented in Table2 . It is worth noting that the number of computational steps executed by the Rete algorithm for pattern matching each query are... ..."

### Table 2: The relative errors in depth computation using our invariant algorithm, for a ne and rigid shape. Rigid Invar. Rigid random A . Invar A . random 8:4% 27:6% 2:9% 23:3%

1995

"... In PAGE 6: ... 3.) Table2 summarizes the results of our invariant algo- rithm for the last 8 points. Due to the noise in the data and the large perspective distortions, not all the frames were consistent with rigid motion.... ..."

Cited by 56