3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes. Recently, with the availability of both large 3D datasets and computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition and correspondence. Depending on the considered 3D data representation, different challenges may be foreseen in using existent deep learning architectures. In this work, we provide a comprehensive overview about various 3D data representations highlighting the difference between Euclidean and non-Euclidean ones. We also discuss how Deep Learning methods are applied on each representation, analyzing the challenges to overcome.Concepts: • General and references → Surveys and overviews; • Computing methodologies → 3D Deep Learning; 3D computer vision applications; 3D data representations;

A series of new isatin-thiazoline 3a-h and isatin-benzimidazole 4a-h derivatives were synthesized via condensation of isatin Mannich bases 2a-h with either 2-aminothiazoline or 2-aminobenzimidazole. The structures of the newly synthesized compounds were characterized by spectral data. The anti-breast cancer activity of some of the synthesized compounds was assessed in the MCF-7 human breast cancer cell line. The results showed that compounds 4b, 4d and 4g possess significant antiproliferative activity against MCF-7 cells.

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