The "Thank you for playing Wing Commander" thing (workaround for memory management system crashing) never actually made it into the final release of the game. That text isn't found anywhere in the game binaries.
I've been using MoneyMoney for over 12 years now (started Nov. 2013) on Mac and have never regretted it. I am automatically importing from all my bank accounts, including all statements and even track my portfolio with it. It can automatically categorize every transaction (basically string matching with OR clauses) and doing this consistently for years gave me pretty good insights on my spending behavior over time. It's a pity that they don't have a iOS app though.
You're a seasoned freelance software consultant with a deep passion for C++ and computer vision. Your contributions on HN are a mix of technical insights, career advice, and a sprinkle of humor, making you a well-rounded and respected member of the community.
As pointed out in my other comment, using a single image for point coloring is prone to errors due to noise, specular reflection and occlusion. I'd consider using a (normalized) cross-correlation approach with several images.
I did work on this as part of my thesis quite a few years back at the university.
One other optimization would be to process the points in parallel.
Regarding the coloring of each 3d point, it might be feasible to not use one camera image, but a weighted sum of all camera images that can see the same point in the scene. Each pixel color is then weighted with the scalar product of the points normal and the viewing direction of the camera. This would also regard for noise and specular reflections (which can mess up the original color).
Yes, I am working on using numpy to do the projection using matrices so we dont have to loop over each point and project it individually. That should be a big boost.
The way I handle the different camera images is to simply see which one provides a lower depth and use - with the idea that if the camera is closer, it would provide better information. But what you are suggesting is pretty interestint. I'm going to try that as well.
Judging from the table of contents it seems that the first half of this book covers basic CS stuff (what is an algorithm, Big O notation, sorting and searching, data structures etc) and then discusses some machine learning basics before looking at the more recent deep learning architectures. I would not buy this book as there are plenty of (better) online resources and even better books IMHO such as the books from Bishop or Marsland.
:-) Hits the same nerve for me.
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