IEEE Computational Intelligence Magazine - November 2023 - 73

TABLE I Results obtained for the testing point clouds from the full models on ModelNet40 in the full-category setting [44]. Bold
indicates the best result and underline indicates the second-best result.
METHOD
ICP [20]
FGR [40]
FPFH [41]
PointNetLK [11]
FMR [39]
PCRNet [30]
PRNet [18]
DCP-v2 [15]
IDAM-GNN [29]
RPMNet [28]
OMNet [42]
CFNet(ours)
(a) UNSEEN MODELS
(b) UNSEEN CATEGORIES
(c) GAUSSIAN NOISE
RMSE(R) MAE(R) RMSE(t) MAE(t) RMSE(R) MAE(R) RMSE(t) MAE(t) RMSE(R) MAE(R) RMSE(t) MAE(t)
12.353 5.617 0.1668 0.0695 13.298 6.241 0.1834 0.0789 13.828 7.117 0.2902 0.0878
7.381
3.381
9.259
6.852
4.220
3.205
2.926
2.838
1.634
1.133
1.106
5.381 0.0117 0.0062 7.344
2.961 0.0152 0.0132 3.213
5.363 0.0131 0.0068 7.387
2.821 0.0158 0.0134 5.067
0.0105
0.911 0.0097 0.0056
1.006
0.0025 2.484
2.094
1.803 0.5667 0.475 7.633
2.885 0.0412 0.0469 4.524
2.315 0.0218 0.0171 4.328
3.207 0.0236 0.0127 3.083
0.715 0.0251 0.0047 3.671
0.539
0.0169
1.193 0.0206 0.0146
0.0036 0.0024 1.272
by Gaussian noise, which will be discussed
in detail in Section IV-E.
B. Full Categories for Training &
Testing
In the first experiment, all categories of
point clouds in ModelNet40 are used.
1.024
0.0045 2.217
1.583
5.356 0.0105 0.0058
4.196 0.0215 0.0188
1.253 0.5764 0.4763 11.045 2.628 0.5761 0.4764 12.177 2.865 0.6673 0.5374
1.596 0.5851 0.4886 7.579
3.015 0.0458 0.0484 4.895
1.415 0.0168 0.0124 4.995
1.273 0.0214 0.0123 4.258
0.742 0.0128 0.0034 2.462
0.287
2.295 0.5797 0.4844
3.257 0.0468 0.0505
2.056 0.0170 0.0123
1.277 0.0105 0.0091
1.026 0.0236 0.0061
0.570
1.162
0.0056
0.0032 0.0027 1.348
9843/2468 samples are used for training
and testing, respectively. Table I(a) evaluates
the performance of our method
and its counterparts in this experiment
(ICP and PointNetLK almost fail).
Note that the unsupervised-based registration
methods [36], [37] outperform
1.234
0.0033
0.0028
most supervised-based registration
methods in terms of most metrics, and
our method can theoretically further
stimulate their potential. From the
experimental results, CFNet is superior
to the other methods under most performance
indicators, showing strong
0.0161 0.0048
0.0024
FIGURE 6. Registration results (cyan: source point cloud, magenta: target point cloud, and gray: transformed point cloud). The first two are
correspondence- or feature descriptor-based methods, and the last three are deep learning-based methods where the target point cloud is the same
but the initial poses of the source point clouds are different to distinguish the registration effects of the different methods.
NOVEMBER 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 73

IEEE Computational Intelligence Magazine - November 2023

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