JayFoxRox

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).

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karpathy

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

natowi

shubhamwagh

Steps to create Textured Mesh from Point Cloud using Meshlab

Get your PointCloud into MeshLab

• Import the pointcloud file in ".ply" file format in Meshlab. Before importing make sure you do some pre-processing / cleaning on point cloud so as to ease the process of meshing.

Point Cloud Simplification and Normals Computation

• Next we need to reduce the number of point samples for smooth meshing.
  • So go to Filters -> Point Set -> Point Cloud Simplification. Enter Number of samples circa 5% of original number of points. Make sure Best Sample Heuristic is checked.
• After point cloud simplification, make sure to select Simplified point cloud in the Show Layer Dialog on the right hand side. If not visible, it can be opened by navigating to View -> Show Layer Dialog. Now we need to compute normals for point set.
• So go to Filters -> Point Set -> Compute normals for point sets . Enter Neighbour num between 10 - 100. Initially try with 10 and

donmahallem

johnhw

### JHW 2018
import numpy as np
import umap



# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
    
import random

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

simonw

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

agzam

e[dit]	evil-edit
w[rite]	evil-write
wa[ll]	evil-write-all
sav[eas]	evil-save
r[ead]	evil-read
b[uffer]	evil-buffer
bn[ext]	evil-next-buffer
bp[revious]	evil-prev-buffer
bN[ext]	bprevious
sb[uffer]	evil-split-buffer

losisli

测试平台：DigitalOcean VPS ubuntu14.04 x64, strongswan5.2.2

运行以下命令请使用root权限

一：安装strongswan

由于ubuntu软件仓库中strongswan版本较低，因此从官网源码编译安装

apt-get install build-essential     #编译环境
aptitude install libgmp10 libgmp3-dev libssl-dev pkg-config libpcsclite-dev libpam0g-dev     #编译所需要的软件