Notes about gsplat / 3DGS: 3D Gaussian Splatting (~WIP)
I'm planning to turn this into an "awesome-gsplat" repository (https://github.com/sindresorhus/awesome) or a wiki on some repository.
Feel free to add comments to this gist for now.
I'm contiuining to update this, and will hopefully get to reformatting it with thumbnails and further information (especially on performance and capabilities of implementations).
| import os | |
| import subprocess | |
| import sys | |
| import xml.etree.ElementTree as ET | |
| with open(sys.argv[1], 'r') as rss: | |
| et = ET.parse(rss) | |
| root = et.getroot() | |
| for name, url in zip((e.text for e in root.findall('./channel/item/title')), (e.attrib['url'] for e in root.findall('./channel/item/enclosure'))): | |
| download_name = f"{name.replace(' ', '-')}.mp4" |
This quick guide for getting a Jupyter Notebook up and running on Bridges, a supercomputer managed by the Pittsburgh Supercomputing Center. Bridges is a new machine designed to accommodate non-traditional uses of High Performance Computing (HPC) resources like data science and digital humanities. Bridges is available through XSEDE, which is the system that manages access to multiple supercomputing resources. Through XSEDE, Bridges is available researchers or educators at US academic or non-profit research institutions (see the XSEDE eligibility policies) Allocations are free, but there is a somewhat difficult to understand application process filled with jargon and acronyms that take time to understand. See the XSEDE getting started guide for more information about getting acc
