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# Working with 3D-graphics *

It's time to render!

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## A Second Check of Newton Game Dynamics with PVS-Studio

Some time ago, somewhere on the Internet, I stumbled upon a physics engine called Newton Game Dynamics. Knowing that engine projects are usually big and complex, I decided to check its code with PVS-Studio for any interesting defects. I was especially enthusiastic about this one because my co-worker Andrey Karpov already checked it in 2014 and a second check would be a good opportunity to demonstrate our analyzer's evolution over the past six years. As of this writing, the latest version of Newton Game Dynamics is dated February 27, 2020, which means it has been actively developing for the past six years too. So, hopefully, this article will be interesting not only to us but to the engine's developers as well – and for them it's a chance to fix some bugs and improve their code.
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## Ray Cast Visual Search (RCVS). Fast and simple algorithm for searching 3D objects with similar shapes

For me, these two models are quite similar, but in fact they don’t have obvious characteristics to measure this similarity. These models have different numbers of vertices, edges and polygons. They are of different sizes, rotated differently and both have the same transforms (Location = [0,0,0], Rotation in radians = [0,0,0], Scale = [1,1,1]). So how to determine their similarity?
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## Polygonal Mesh to B-Rep Solid Conversion: Algorithm Details and C++ Code Samples

Boundary representation (B-rep) is the primary method of representing modeled objects in most geometric kernels, including our C3D Modeler kernel. The core algorithms that edit models, such as applying fillet operations, performing cutting operations, and obtaining flat projections require the precision of B-rep representations. The rapidly growing variety of 3D data in polygonal formats makes the task of model transformations from polygons into boundary representation increasingly relevant. As a result, we developed a new SDK, C3D B-Shaper, which is part of our C3D Toolkit.

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## Automatic respiratory organ segmentation

Manual lung segmentation takes about 10 minutes and it requires a certain skill to get the same high-quality result as with automatic segmentation. Automatic segmentation takes about 15 seconds.

I assumed that without a neural network it would be possible to get an accuracy of no more than 70%. I also assumed, that morphological operations are only the preparation of an image for more complex algorithms. But as a result of processing of those, although few, 40 samples of tomographic data on hand, the algorithm segmented the lungs without errors. Moreover, after testing in the first five cases, the algorithm didn’t change significantly and correctly worked on the other 35 studies without changing the settings.

Also, neural networks have a disadvantage — for their training we need hundreds of training samples of lungs, which need to be marked up manually.

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## WBOIT in OpenGL: transparency without sorting

This post is about weighted blended order-independent transparency (WBOIT) — the trick that was covered in JCGT in 2013.
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