代码拉取完成,页面将自动刷新
Created by Yangyan Li, Soeren Pirk, Hao Su, Charles Ruizhongtai Qi, and Leonidas J. Guibas from Stanford University.
We propose a light-weight way for learning features from 3D data. See more details from our research paper on arXiv (was accepted to NIPS 2016).
Check training settings for example usage of the field probing layers, as well as logs generated during our training.
If you are interested in FPNN, we highly recommend you take a look at PointCNN, which outperforms FPNN in terms of ModelNet40 classification, together with other advantages.
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。