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@@ -62,7 +62,7 @@ <h1>Ray Tracing and Path Tracing</h1>
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During the third and last year at ESIR, I participated in an <aclass="finger bae-rock inline" href="en/gallery/raytracing/#industrial">industrial project</a> about image synthesis as well.
We added realtime support in our application, which permitted us to debug and place components easily. It also helped to choose precisely rendering options. This real time feature achieve to have good performances thanks to an automatic decrease of the number of sent rays when moving the camera. The image will auto scale back to normal on stop, allowing an infinite number of passes and antialiasing.
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Last year at ESIR embedded an industrial project proposed by an enterprise. The latter where I participated was proposed by Chaos Czech which develop the <ahref="https://corona-renderer.com/">Corona Renderer</a>. This project driven by four persons consists in implementing Optimal Multiple Importance Sampling in a bidirectional path tracer. It is also totally necessary to be unbiased. Here are presented the steps of the project, beginning with a small skill upgrading embedding Multiple Importance Sampling, Light Tracing and Bidirectional Path Tracing.
During the summer of 2019, I worked as an intern for 3 months in <ahref="https://limu.ait.kyushu-u.ac.jp/" target="_blank">LIMU</a> research laboratory of Kyūshū University in Fukuoka, Japan. This internship was supervised by associate professor <ahref="https://limu.ait.kyushu-u.ac.jp/~uchiyama/me/index_e.html" target="_blank">Hideaki Uchiyama</a>. I mostly worked in the building of a framework for indoor localization.
The main goal of this internship was to participate to one of the tracks of the <ahref="https://ipin-conference.org/2019/cfc.html" target="_blank">competition</a> organized by IPIN 2019. Hence, every work, such as documentation, research or implementation, lead to a framework solving the problem raised by this competition. This competition is divided into five tracks focusing on different indoor localization problems. I participated in the fifth track and won the first prize on the manufacturing part. For the Restaurant part, I only finished second despite my best results because the SOE metric was presented after the end of my internship so I ended up with 0.0 on this category.
The inputs are composed of all PDR information (acceleration and rotation) and Bluetooth Low Energy (BLE) signals received. The framework developed only rely on the latest, BLE.
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