Growing into a professional in the field of technology means striving with studies and engaging oneself in the industry. For many students, this means working as a part-time summer trainee at a company. Among all the positive experiences, the responsibilities can sometimes prove to be quite an ordeal during work and studies. This is where the support of the employer plays an important role.
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Display Color Properties
Displays of all kinds are the main channels for human interaction with various devices. Whether it is a mobile phone, tablet, PC, cars, home appliances, info screens, or machine controllers, displaying information and content on display is the most natural way to interact with people. As the variety and usage of displays increase, the color optics of display performance grows in importance. Can the user see the content clearly from various angles? Are the colors vivid and natural at the same time? Are there unevenness or visible defects on display? Do the colors of two in-device displays match when it comes to dual-screen devices?
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Each year we seek enthusiastic, bold and open-minded Trainees to join our troops. This summer was no different and we were excited to have overall six new OptoFidelitians. We promised that these Trainees will have a summer with cool robots, awesome colleagues and unique projects – how well did we succeed with this promise? Let’s find out from the authors themselves: Sini, Joonas, Olli-Pekka, Ismo and Juho!
Sini and Joonas, you worked as Software Trainees, what did you get to do here?
Sini: During the summer I got to do a lot of work directly related to my education and interests. This was the most important thing for me, since as someone mostly focused on users, UX and UI design back-end coding is not inspiring to me. I was surprised how much people here were eager to hear my opinions and how much those really affected the outcome of the projects.
I designed and made prototypes of a few user interfaces and got to develop one from start to finish. In the university I hadn’t learned a lot about UI programming and the experience from here is patching up important holes in my know-how.
What really made the summer enjoyable here was my project team. The average age of the team was probably somewhere in the mid to late twenties, which helped us connect and have fun too on the side of work. I look forward to continuing as a part-timer when I start my studies again!
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We at OptoFidelity are happy to introduce a new image quality testing solution for AR/VR head mounted devices – OptoFidelity™ GoldenEye HMD IQ!
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Maturity of AR/VR/MR devices is improving significantly during 2019. In order to offer seamless UX for consumers, more comprehensive testing methods are needed. A novel content tracking method for AR/VR/MR testing purposes is introduced by OptoFidelity, the leading test solution provider for HMD UX testing.
In 2019, we can expect a wealth of exciting virtual and augmented reality devices arriving: for example, Oculus is going to release the standalone Quest headset, and Nreal raised $15M of funding to produce a sunglass-sized AR headset. In CES 2019, there were almost a hundred exhibitors in the AR/VR Gaming category.
Quick development of the new technology gives rise to the need to verify performance in product development as a part of continuous integration. This post focuses on measuring head tracking accuracy, which is comprised of many measurable elements: drifting, jitter, motion-to-photon latency, cross-axis coupling… you name it!
Kick-start for HMD UX testing was done in 2017, when OptoFidelity´s first offering BUDDY-1, previously known as VR Multimeter was launched. BUDDY-1 is a solution for benchmarking the motion-to-photon latency with one degree of freedom. Our BUDDY testers are equipped with a smart camera which captures and analyzes the frames displayed on the headset. BUDDY-1 tracks the optical flow of a target pattern placed in the virtual world and compares that to the physical rotation angle over time, yielding motion-to-photon time. BUDDY-1 is a good work horse for basic motion-to-photon latency measurement e.g. to catch some fatal performance regressions.