| Title | : | The 3D natural science Paradise: deep sea monster |
| Author | : | ( YING ) HUO KE KE ( David Hawcock ) . ( YING ) Claire Bampton |
| Language | : | en |
| Rating | : | 4.90 out of 5 stars |
| Type | : | PDF, ePub, Kindle |
| Uploaded | : | Apr 07, 2021 |
Full Download The 3D natural science Paradise: deep sea monster - ( YING ) HUO KE KE ( David Hawcock ) . ( YING ) Claire Bampton file in ePub
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The 3d natural science paradise: deep sea monster(chinese edition) (chinese) hardcover – january 1, 2013 by ( ying ) huo ke ke ( david hawcock ) ( ying ) claire bampton (author).
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This research was also partially supported by the national key research and development program of china (2016yfc1400704), national natural science foundation of china (u1613211 and u1713208), shenzhen basic research program (jcyj20170818164704758 and cxb201104220032a), the joint lab of cas-hk, and shenzhen institute of artificial intelligence.
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In general, the develop-ment of deep learning for 3d object is closely related to the progress of representation form of 3d object from geomet-ricregulardatatoirregularone. Fortheconventionalcnns, it is intractable to handle the geometric irregular data, such asmeshesandpointclouds.
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The 3d deep learning network had two independent input channels that corresponded to these two point clouds. (11772015), and the national natural science foundation of china (11832003.
However, semantic learning of 3d point clouds based on deep learning is challenging due to the naturally unordered data structure. In this work, we strive to impart machines with the knowledge of 3d object shapes, thereby enabling machines to infer the high-level semantic information of the 3d model.
A motivation to use deep learning based approach is that it is difficult to manually define a measurement for saliency. If we use existing 3d shape descriptors such as gaussian curvature and normal information, the features may depend on the human-specified. So, we leverage the strength of deep learning such that features with human labels.
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