Robust Object Classification Approach Using Spherical Harmonics
نویسندگان
چکیده
Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying objects. The proposed framework first uses the voxel grid of concentric spheres learn features over unit ball. We then limit order level suppress effect In addition, entire classification operation is performed in Fourier domain. As a result, our model learned that less sensitive data perturbations corruptions. tested against several types corruptions, such as Our results show has fewer parameters, competes with state-of-art networks terms robustness inaccuracies, faster than other methods. implementation code also publicly available at https://github.com/AymanMukh/R-SCNN
منابع مشابه
Spherical Harmonics
5 Spherical Harmonics 7 5.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5.2 Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5.2.1 Harmonic expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5.2.2 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3151350