This project is an aid to the blind. Till date there has been no technological advancement in the way the blind navigate. So I have used deep learning particularly convolutional neural networks so that they can navigate through the streets.
- Assistant Professor at School of Information Science and Technology, ShanghaiTech, Shanghai
- PhD from ETH Zurich
- author of OpenGV
- inviting PhD Applications
r/cad: ***Computer-Aided Design*** A place to talk about anything related to CAD. Ask questions about CAD software, drawing standards or just show off your latest project.
Did Germany experience rapid industrial expansion in the 19th century due to an absence of copyright law? A German historian argues that the massive proliferation of books, and thus knowledge, laid the foundation for the country's industrial might.
I’m sort of obsessed about iteration speed. I’ve written about this in the past and it deserves more posts in the future, but the quick summary is that iteration speed is always going to be the strongest competitive advantage in this industry. There’s of course many ways we can iterate faster, but for today let’s focus on two particular aspects of it: testing and deploying more often.
For something that we spend a third of our lives doing (if we’re lucky), sleep is something that we know relatively little about. “Sleep is actually a relatively recent discovery,” says Daniel Gartenberg, a sleep scientist who is currently an assistant adjunct professor in biobehavioral health at Penn State. “Scientists only started looking at sleep...
This is an undergraduate textbook suitable for linear algebra courses. This is the only textbook that develops the linear algebra hand-in-hand with the geometry of linear (or affine) spaces in such a way that the understanding of each reinforces the other. The text is divided into two parts: Part I
W. Lavrijsen, and A. Dutta. Proceedings of the 6th Workshop on Python for High-Performance and Scientific Computing, page 27--35. Piscataway, NJ, USA, IEEE Press, (2016)
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser. (2016)cite arxiv:1603.08182Comment: To appear at the Conference on Computer Vision and Pattern Recognition (CVPR) 2017. Project webpage: http://3dmatch.cs.princeton.edu.