@snapdragon #capturethedragon #freetablets #sxsw #gotyousucka twitter.com/AndyCheeks/sta… — Cheeks (@AndyCheeks) March 13, 2013

Our brand ambassadors, Josh and Jess:

The band Surfer Blood plays to a packed crowd at Antone's Nightclub.

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• See our full SXSW photo gallery at our Snapdragon Facebook page.
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Qualcomm® Snapdragon™ processors sponsored the Pandora Discovery Den where they showcased a great and eclectic mix of bands. If you couldn't make it to SXSW this year, you can still hear all the music on the Pandora Discovery Den mixtape.

Jason works on the digital marketing team for Snapdragon, including the website, online advertising, and social media. Prior to working at Qualcomm, he led digital efforts for brands such as Bank of America and TaylorMade-adidas Golf.

Jason has a bachelor's degree in History from The George Washington University in Washington, DC, and has no intention of ever moving away from San Diego.

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries (\"Qualcomm\"). The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

Jason works on the digital marketing team for Snapdragon, including the website, online advertising, and social media. Prior to working at Qualcomm, he led digital efforts for brands such as Bank of America and TaylorMade-adidas Golf.

Jason has a bachelor's degree in History from The George Washington University in Washington, DC, and has no intention of ever moving away from San Diego.

Our host PJ is a recent convert to the importance of ping. He’s joined by our modem expert, Sherif Hanna, to explain how it plays a major role in everything from gaming to self-driving cars, and why 5G will take ping to the next level.

· Closely related link: Qualcomm Snapdragon Gigabit LTE modems

· Loosely related link: The Hunt for Red October

^{Qualcomm Snapdragon is a product of Qualcomm Technologies, Inc.}

Our host PJ is a recent convert to the importance of ping. He’s joined by our modem expert, Sherif Hanna, to explain how it plays a major role in everything from gaming to self-driving cars, and why 5G will take ping to the next level.

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries (\"Qualcomm\"). The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

We envision a world where devices, machines, automobiles, and things are much more intelligent, simplifying and enriching our daily lives. They will be able to perceive, reason, and take intuitive actions based on awareness of the situation, improving just about any experience and solving problems that to this point we’ve either left to the user, or to more conventional algorithms.

Artificial intelligence (AI) is the technology driving this revolution. You may have heard this vision or may think that AI is really about big data and the cloud, and yet Qualcomm’s solutions already have the power, thermal, and processing efficiency to run powerful AI algorithms on the actual device — which brings several advantages.

AI is a pervasive trend that is rapidly accelerating thanks to vast amounts of data and progress in both algorithms and the processing capacity of modern devices. New technology can seem to appear out of nowhere, but oftentimes researchers and engineers have been toiling over it for many years before the timing is right and key progress is made.

At Qualcomm, we have a culture of innovation. We take pride in researching and developing fundamental technologies that will change the world at scale. AI is no different. We started fundamental research more than 10 years ago, and our current products now support many AI use cases, such as computer vision, natural language processing, and malware detection — both for smartphones and autos — and we are researching broader topics, such as AI for wireless connectivity, power management, and photography.

We have a deep machine learning heritage

We have a long history of investing in machine learning. In 2007, we started exploring spiking neuron approaches to machine learning for computer vision and motion control applications. We then expanded the scope of the research to look not just at biologically inspired approaches but artificial neural networks — primarily deep learning, which is a sub-category of machine learning. Time and time again, we saw deep learning-based networks demonstrating state-of-the-art results in pattern-matching tasks. A notable example was in 2012 when AlexNet won the prestigious ImageNet Challenge using deep learning techniques rather than traditional hand-crafted computer vision. We’ve also had our own success at the ImageNet Challenge using deep learning techniques, placing as a top-3 performer in challenges for object localization, object detection, and scene classification.

We have also expanded our own research and collaborated with the external AI community into other promising areas and applications of machine learning, like recurrent neural networks, object tracking, natural language processing, and handwriting recognition. In September 2014, we opened Qualcomm Research Netherlands in Amsterdam, a hotbed for machine learning research, and have continued to work closely with Ph.D. students working on forward-thinking ideas through our Qualcomm Innovation Fellowship program. In September 2015, we established a joint research lab with the University of Amsterdam (QUVA) focused on advancing the state-of-the-art in machine learning techniques for mobile computer vision. We further deepened our relationship with Amsterdam’s AI scene by acquiring Scyfer, a leading AI company in Amsterdam. Max Welling, a Scyfer founder, is a renowned professor at the University of Amsterdam, where he has focused on machine learning, computational statistics, and fundamental AI research. Scyfer focuses on applying a wide range of machine learning approaches to real-world problems. The Scyfer team will join Qualcomm Research’s machine learning group.

Superior power and performance for on-device machine learning

To make our vision of intelligent devices possible, we also knew that the machine learning-based solutions would need to run on the device — whether a smartphone, car, robot, drone, machine, or other thing. Running the AI algorithms — also known as inference — on the device versus in the cloud has various benefits such as immediate response, enhanced reliability, increased privacy, and efficient use of network bandwidth.

The cloud remains of course very important and will complement on-device processing. The cloud is necessary for pooling of big data and training (currently) that results in many of the AI inference algorithms that run on the device. However, in many cases, inference running entirely in the cloud will have issues for real-time applications that are latency-sensitive and mission-critical like autonomous driving. Such applications cannot afford the roundtrip time or rely on critical functions to operate when in variable wireless coverage. Further, on-device inference is inherently more private.

We are not limiting ourselves to only running inference on the device. We also research training AI on the device for targeted use cases such as gesture recognition, continuous authentication, personalized user interfaces, and precise mapping for autonomous driving — in a synergistic cooperation with the cloud. In fact, we have a unique ability to explore future architectures which can benefit from high-speed connectivity and high-performance local processing, resulting in the best overall system performance.

Running efficient on-device AI requires heterogeneous computing

For over a decade, Qualcomm has been focused on efficient processing of diverse compute workloads within the power, thermal, and size constraints of mobile devices. Qualcomm Snapdragon Mobile Platforms have been the SoC of choice for the highest performance mobile devices. AI workloads present another challenge in this regard. By running various machine learning tasks on the appropriate compute engines — such as the CPU, GPU, and DSP — already in our SoC, we offer the most efficient solution. A key example is the Qualcomm Hexagon DSP, which was originally designed for other vector math-intensive workloads but is being further enhanced to address AI workloads. In fact, the Hexagon DSP with Qualcomm Hexagon Vector eXtensions on Snapdragon 835 has been shown to offer a 25X improvement in energy efficiency and an 8X improvement in performance when compared against running the same workloads (GoogleNet Inception Network) on the Qualcomm Kryo CPU.

The diversity in architecture is essential and you can’t rely on just one type of engine for all workloads. We will continue to evolve our existing engines for machine learning workloads to maintain our lead in maximum performance per watt. Leveraging our research into emerging neural networks, we are well positioned to extend our heterogeneous computing capabilities to address future AI workloads with a focus on maximum performance per watt. In fact, we envisioned dedicated hardware for running AI efficiently back in 2012.

We are democratizing AI at scale

Making heterogeneous computing easy for developers is hard. It is not enough just to have great hardware. To bridge that gap, we have introduced the Snapdragon Neural Processing Engine (NPE) Software Developer Kit (SDK). This features an accelerated runtime for on-device execution of convolutional neural networks (CNN) and recurrent neural networks (RNN) — which are great for tasks like image recognition and natural language processing, respectively — on the appropriate Snapdragon engines, like the Kryo CPU, Qualcomm Adreno GPU, and Hexagon DSP. The same developer API provides access to each of our engines so developers can easily switch their AI tasks from one to another seamlessly.

The Neural Processing Engine also supports common deep learning model frameworks, such as Caffe/Caffe2 and TensorFlow. The SDK is a lightweight, flexible platform designed to deliver optimal performance and power consumption by leveraging Snapdragon technology. The SDK is designed to enable developers and OEMs in a broad range of industries, from health care to security, to run their own proprietary neural network models on portable devices. As an example, at this year’s F8 conference, Facebook and Qualcomm Technologies announced a collaboration to support the optimization of Caffe2, Facebook’s open source deep learning framework, and the NPE framework.

Continued research to expand our AI scope and bring increased efficiency

We are in the early days of machine learning journey and deep learning is just one of many machine learning technologies that has the potential to transform computing.

To pursue even more sophisticated applications, we continue to advance on multiple fronts:

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• Specialized hardware architectures: A continued focus on low-power hardware, whether enhanced, specialized, or custom, to handle these machine learning workloads
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• Advances in neural network approaches: Research related to training with semi-supervised approaches, unsupervised approaches like generative adversarial networks (GANs), distributed learning, and privacy protecting approaches
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• Network optimization for on-device applications: Research related to compression, inter-layer optimizations, optimizations for sparsity, and other techniques to take better advantage of memory and space/time complexity
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The opportunities for always-on, intelligent devices that do all or most of their thinking on the device are enormous, and we look forward to advancing state-of-the-art machine learning through both research and productization. Today, the Qualcomm Artificial Intelligence Platform provides highly responsive, highly secure, and intuitive user experiences through efficient on-device machine learning. The future holds much more promise.

Stay tuned for future blog posts about our neural processing architecture research and applications of machine learning, such as enhanced connectivity, power management, and better photography. If you are excited about solving big problems with cutting-edge technology and improving the lives of billions of people, we’d like to hear from you.

AI is a pervasive trend that is rapidly accelerating thanks to vast amounts of data and progress in both algorithms and the processing capacity of modern devices. New technology can seem to appear out of nowhere...

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries (\"Qualcomm\"). The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

Depth sensing cameras are becoming mission critical to advances in image quality, extended reality (XR), and security. Today Qualcomm Technologies is unveiling a new image signal processor (ISP) and line of camera modules to accelerate depth sensing in smartphones, XR headsets, and more.

Qualcomm Spectra ISP, 2^nd Generation

The Qualcomm Spectra ISP is designed to communicate with your camera module. Generally speaking, the ISP dictates the speed your camera can focus and snap a photo, and the photo’s image quality. The 1st generation of Qualcomm Spectra ISP was integrated into the Qualcomm Snapdragon 820 and 835 platforms, which allowed for beautiful photos in an instant.

Today we’re unveiling our 2^nd generation Qualcomm Spectra ISP. It features a completely new architecture that is engineered to increase image quality and speed, but more importantly, it’s designed for depth sensing in high-resolution and high accuracy — at very low-power. Additional features and details will be released later this year. In the meantime, below are a few we’re announcing today:

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  Superior photographic quality with multi-frame noise reduction and super resolution

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  Improved camcorder video quality with motion compensated temporal filtering (MCTF)

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  Inline gyro-based electronic image stabilization (EIS) to reduce unwanted vibrations when recording videos

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  Machine learning accelerated computer vision for advanced used cases like face detection and improved bokeh photography

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  Accurate simultaneous localization and mapping (SLAM) for XR use cases

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Spectra Module Program

The Spectra Module Program (SMP) was launched last year, to give our customers optimized single and dual camera module solutions. This helps them bring devices with great low light photography and smooth-zoom video recording to commercialization quickly. These modules are now widely deployed in many 2016 and 2017 flagship smartphones. Now we’re expanding our SMP with new camera modules capable of utilizing active sensing for improved biometric authentication, and structured light for a variety of computer vision applications that require real-time, dense depth map generation, and segmentation.

 The premium active depth sensing module will use structured light to produce incredibly accurate high-resolution depth maps. You can see it in action in the above video. You’ll see the depth map can be viewed at different angles in real-time. The depth map contains over 10,000 points of depth and can detect changes in depth as small as .1mm apart.

 Later this year Qualcomm Technologies will announce more features and details in the 2^nd Generation Qualcomm Spectra ISP. Stay tuned.

Depth sensing cameras are becoming mission critical to advances in image quality, extended reality (XR), and security. Today Qualcomm Technologies is unveiling a new image signal processor (ISP) and line of camera modules to accelerate depth sensing in smartphones, XR headsets, and more.

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries (\"Qualcomm\"). The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

As more “things” become connected, the amount of personal data (health, financial, and identification information) at our fingertips is increasing exponentially. With 5G in our near future, the number of connected devices will only continue to grow, as will those trying to gain access to them. This is why mobile cybersecurity is more essential than ever.

We sat down with the Executive Vice President of Qualcomm Technologies, Inc. and President of Qualcomm CDMA Technologies, Cristiano Amon, to discuss the growing importance of mobile device security:

Why is there a growing concern about mobile cybersecurity?

Simply put: it’s about the numbers. The smartphone and its mobile ecosystem are the largest technology platform ever built — way bigger than PCs. In the next four years, more than 8.5 billion smartphones are projected to ship, according to Gartner. That’s an extraordinary figure.

The mobile ecosystem now touches almost every industry, from banking to healthcare to gaming and beyond. And it’s not just our devices, which house our most personal information. It’s the services built on top of them. Securing this content and these services is essential.

So it’s not just about securing the smartphone?

Correct. It’s not about one device or one system. Rather, security practices and design need to be woven through a system, from device to the operator’s network, and finally to the service provider in the cloud. That said, securing the device is essential to securing the overall system.

And how would we go about securing the overall system?

I’m a true believer that passwords have long been ineffective. They’re fairly easy to hack, and they’re easy to forget. To ensure robust authentication, the first step would be to eliminate them, or at least minimize the use of them. From there, devices can use a combination of more reliable forms of verification that establish comprehensive device security.

You mention “robust authentication.” Is that how we support device security?

Yes, and Qualcomm Technologies is engineering the technology that can do this. Our Qualcomm Mobile Security platform is designed to provide three layers of security at the chip, device, and system levels. It’s engineered to use hardware protections to more securely authenticate the user, validate a device’s location, and confirm that the device isn’t compromised. With this foundation, effective cybersecurity is achievable.

Would you give us some more details about the three steps in authentication?

The first step is authenticating the user. To do this, the device can use biometric technology, such as Qualcomm Fingerprint Sensors, to determine the ridges of a finger precisely and in the future, even map a finger’s sweat pores and blood vessels. And remember, we need to ensure that the authenticator software is running the hardware-protected region on the Snapdragon, and that the user’s “fingerprint template” data is then stored in a part of the storage system designed such that a malware application on the main operating system couldn’t access it. Even in authenticating a user, there are multiple hardware-protected features to enforce the security and privacy of the user.

Other key trends in authentication are iris and facial recognition that works with a device’s front-facing camera, and voiceprint technology, which uses the device’s microphone. These are starting to appear in more consumer devices, but I expect that in the near future, a user will come to expect and rely on multi-factor authentication that utilizes all of these technologies. For example, accessing your banking application would require your fingerprint, but transferring funds would require an iris or face scan. And if you’re transferring a large amount, the smartphone’s microphone would also ask you to verbally authenticate with a simple, “transfer approved.” In the future, we expect to see smartphones that advance to this level of security.

Next, a key consideration is validating the device’s location and doing so directly from the hardware from the secure mode of the device. Qualcomm Technologies’ positioning technology uses GPS, Wi-Fi, Bluetooth, and cellular connections to help support the integrity of the position. The signal processing and position calculation occur in the Qualcomm Snapdragon modem mobile platform, to help protect the information against tampering or fabrication.

To help confirm that the device hasn’t been compromised, one needs hardware-based device attestation, which is just a way to say ‘device authentication.’ This establishes both the actual hardware-based device ID as well as the “health” of the device. As an example, the cloud would check the device ID, the software versions installed, its location and time, and the integrity of its applications and operating system. This information would then be compared to the information stored in the mobile platform, to prove that the device’s software hasn’t been endangered.

It’s not enough to secure the cloud, the network, or the device individually. For comprehensive authentication, it’s critical to secure all of these endpoints.

What role does machine learning play in device security?

By using machine intelligence, a device can be trained to identify malicious behavior and take appropriate actions against possible threats — all on its own. With machine learning, devices can analyze user software usage in the past and scan for viruses in the software in real-time.

Take the Gooligan attack that happened late last year. The Android malware compromised over one million Google accounts — the result of infected apps downloaded from third-party app stores or phishing scams. The malware was able to get ahold of a lot of consumer data, not just the user’s credit card information. Like I said before, multiple industries touch upon the mobile sphere, and if malware is installed, the user is essentially inviting the vampire into his or her home.

To help prevent this kind of breach, Qualcomm Technologies has placed the mobile platform at the device’s center, where security information, such as data on the user’s fingerprint or GPS location, is removed from the operating system and saved in a security-rich mode of the hardware. Machine learning technologies, such as those found in our Qualcomm Mobile Security product, can then access this information, recognize any changes in usage patterns, and report any potential threats. This is designed to allow products from security app companies to then notify the user and neutralize the threat.

What are the differences and similarities in security solutions for mobile devices and IoT?

Security solutions for mobile devices and IoT devices are more alike than you’d think. For both, cloud-based user- and device-level authentications play integral roles, to help ensure that the device is protected against attacks and false user authentications. Our hardware-based security solutions are also applicable to mobile devices and IoT devices that are powered by Snapdragon as well as other IoT chipsets.

The real difference between mobile and IoT security is scale. The IoT covers a variety of, well, things. Protecting these IoT devices requires a larger array of solutions compared to those for mobile devices. To address this device and cost diversity, we offer a large portfolio of IoT chipsets that are designed to help safeguard personal data and devices, from a simple light bulb to a complex alarm system, to our ubiquitous smartphone.

What’s the biggest challenge for device security in the future?

As the device ecosystem expands, we’ll need to get creative with our security solutions. We’re anticipating more than 23 billion permanently connected “things” by 2021. We won’t only be protecting our smartphones — we’ll be helping to secure body sensors, city infrastructures, and home automation, among other technologies developed for our bodies, homes, and cities. It’s imperative that the information these devices collect is secure and that their security is continually updated. And it all starts with the chip.

To learn more, visit the Qualcomm Mobile Security page, the Qualcomm Product Security team, and read Cristiano’s CTIA presentation on mobile device security.

We sat down with the Executive Vice President of Qualcomm Technologies, Inc. (QTI) and President of Qualcomm CDMA Technologies Cristiano Amon to discuss the growing importance of mobile device security.

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries (\"Qualcomm\"). The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

You didn’t need a crystal ball a few years ago to predict that VR is meant to be mobile and free of wires. It is inevitable and exactly where the industry is heading. We’ve been promoting immersive mobile VR all along and have been driving the industry to solve the extreme challenges to make it possible. A previous blog post explained how important precise, low-latency 6 degrees of freedom (6-DoF) motion tracking is for intuitive interactions with the virtual world — it is essential for the feeling of presence. This blog post will catch you up on the tremendous progress we’ve made in motion tracking and explain the advances we foresee coming soon.

So that we are all on the same page, 3-DoF detects rotational head movements and determines what direction you’re looking, while 6-DoF detects rotational and translational movements, meaning it determines what direction you’re looking as well as where you are in a VR world.

The difference between 3-DoF and 6-DoF is huge in terms of the user experience. With 3-DoF, users feel like they are watching a story from the outside. With 6-DoF, users become part of the story, allowing them to interact and change the story for a much more immersive experience.

Enhancing 6-DoF motion tracking

The VR industry has made a lot of progress toward improved on-device 6-DoF motion tracking. Initial outside-in motion tracking in 2014 used external sensors, markers, cameras, or lasers placed throughout a room to track 6-DoF head movements. Not only was the setup unwieldy, a head-mounted display (HMD) tethered by wire to a fixed location is not immersive.

Outside-in 6-DoF motion tracking evolved to an on-device inside-out solution that requires no room setup. Our initial inside-out solution used a monocular fisheye camera along with inertial sensors as inputs to our visual-inertial odometry (VIO) algorithm to generate a 6-DoF head pose. At CES 2017, we showcased our monocular 6-DoF tracking demo entitled “ZORDS RISING: Power Rangers VR Experience,” where users explored a cinematic 6-DoF environment by making rotational and translational movements without any external tracking mechanisms.

Stereo fisheye cameras are the next step toward better motion tracking. Relative to monocular 6-DoF, stereo 6-DoF provides instant accurate scene depth, faster initialization, better performance with quick and rotational motions, and improved tolerance to camera occlusion. Intuitively this makes sense since two fisheye cameras can see more of the environment, track more feature points, and are less likely to both be blocked by something, such as your hands. Looking forward, room-scale VR will require great motion tracking as well as object detection and boundary augmentation for users to safely move around a large-room environment. The ultimate goal will be world VR, where users will have limitless movement and robust safety in any environment.

No matter the 6-DoF implementation, power efficiency is essential to creating sleek and comfortable headsets. Due to the challenging thermal and power constraints of immersive mobile VR, efficiently running all the parallel workloads is a requirement. Rather than running our VIO algorithm on the CPU, our optimized implementation runs on the Qualcomm Hexagon DSP, which not only saves power and reduces thermal emission, but allows the CPU to process other workloads.

Bringing 6-DoF HMDs and experiences to consumers

Our role in in making mobile VR possible does not end with the silicon. We are driving mobile VR traction forward on both the device and content side. Our HMD Accelerator Program (HAP) helps VR device manufacturers quickly develop premium standalone VR HMDs. Our VR HMD reference design rigorously selects hardware components, such as the cameras and inertial sensors for 6-DoF, that are tuned and validated to support truly immersive VR experiences. Our Qualcomm Snapdragon 835 VR HMD uses stereo cameras for 6-DoF.

Testing to ensure a high-quality VR experience is also part of HAP. When it comes to 6-DoF, we have several key performance indicators that are important to track. For example, we track jitter, which defines the frame-to-frame changes in VIO pose when stationary; absolute error, which defines the total instantaneous error in translation and rotation in the VIO pose for the video sequence; and relative error, which defines frame-to- frame rotation and translation error in VIO pose. It’s also important to measure these KPIs under a variety of real-world conditions, such as seated or standing, soft-to-bright lighting, low-to-high environmental feature points, and slow-to-fast head movements.

To support the development of premium VR content, we also launched the Qualcomm Snapdragon 835 VR Development Kit (VRDK). The VRDK gives developers early access to a Snapdragon 835 VR HMD and an upgraded Snapdragon VR Software Development Kit (SDK). The SDK is designed to provide developers with access to optimized, advanced VR features on Snapdragon VR devices, while abstracting the complexity to create immersive VR experiences. For 6-DoF experiences, the SDK has an API to give access to both the current and future (predicted) head pose.

6-DoF is a game changer when it comes to the VR experience, so developers need to think a bit differently when creating their 6-DoF content. Here are some tips:

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1. Scale and space: Since users can have limitless 6-DoF movements in the virtual world but is confined to a small area (i.e., 4 ft. by 4 ft.) in the real world, design for variable space constraints.
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2. Tracking: Since tracking will sometimes be lost, which is less likely with multiple cameras, fade to black or a fixed image.
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3. Storytelling: Since users may not be looking in the right direction or be positioned in the desired location to advance the story, provide audio and visual cues to guide their focus.
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We’re really excited with the inside-out 6-DoF progress we’ve made to date and look forward to seeing all the amazing 6-DoF VR experiences that developers come up with. Please join our upcoming 6-DoF webinar to learn more.

Learn about the tremendous progress we’ve made in motion tracking as well as the VR advances we foresee coming soon.

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries (\"Qualcomm\"). The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.