Sometimes the question is not how much hardware you can afford to throw at a problem, but whether you are trying to solve the right problem. In short, Leap Motion is a good example to keep in mind when considering the Oculus 'computer vision' vision. No doubt the hardware will be useful, but my expectation is it will not resolve the robustness issues: The 'Dragonfly' specs indicate to me that the company is moving farther and farther away from the original 'breakthrough' and further down the road of general computer vision. There is no reasonable trade-off of hardware cost vs. Ironically, the same heuristics, tailored towards hand tracking, have made tool tracking even worse (at least the last time I looked). There will be glitches, with drastic impact on the user experience. However, you cannot take insufficient data and attempt to fill the gaps by guessing without accepting reliability and robustness issues. For all we know, the original approach described in the patent filings has been abandoned entirely. The solution the company settled on is adding more and more software - kinematics, statistics, heuristics - additional layers of processing the input data. It will break down even in perfect lighting conditions, for poses without occlusion - because hands are much more complex than a cylinder. The key problem Leap Motion has faced ever since the beginning is that Holz' core idea has a very limited applicability - it is perfectly suited for tailor-made tools - and breaks down when applied to hands. In the general sense of 'camera solutions suck, but suck less the more, and more expensive, cameras we use', yes, cost of hardware is an issue, but it is ultimately not what prevents 'decent performance'. Smart cameras would be another example of increased hardware cost - so we could go from 50% added cost (3rd camera) to 2x - 3x cost (more cameras) to multiple Leap Motions (to tackle occlusion - I believe still not supported at even the USB bus level) to adding image processing and pose reconstruction to every single camera. In this narrow sense, cost of hardware could arguably be seen as an issue, but as with all solutions that involve dumb cameras relying on a host PC for processing, the issue is first bandwidth, and (a somewhat more distant second) CPU load. You could also argue that a square of cameras would be better to handle the case where the projection of a finger aligns with the scanline (so the angle will never exceed 45 degrees, and you will have data from both baselines in the worst case). You could argue that, to target fingers, Leap Motion could use 3 cameras arranged on a single baseline (six edges total per scanline per digit intersected, allowing for reconstruction of elliptical cross sections). Plus, the surface properties of skin amplify the issues of background discrimination (another Leap Motion patent is related to this, but a lot less straightforward), which is a completely different type of problem. Worse, fingers do not have constant diameter, and you need to handle the transition to the palm as well (and possibly the wrist). You could argue that fingers can be approximated as ellipses, but unlike a cylinder (fixed radius) you now have to account for major and minor axis as well as an angle against the line of sight - you went from 3 to 6 unknowns against 4 samples, so a single scanline does not reconstruct in full. However, Leap Motion decided to focus on finger tracking as opposed to tool tracking. In fact, a constant diameter cylinder could be reconstructed using edges along scanlines in an even more straightforward manner (intersection angle across scanlines needs to be found as well). retroreflective, appearance against a noisy background). As such, this approach would be very good to track properly shaped tools (with surface material chosen for optimal, e.g. Two cameras will provide 4 samples per scanline, which is sufficient information to reconstruct position (relative to scanline, pixel in row and z depth) as well as radius (a cylinder intersected at an angle will appear to be an ellipse). Historically, Leap Motion as a company is built on an idea by Holz as described in the patent filings: if you use a camera to view a roughly cylindrical object from the side, each scanline will provide two edges. Hardware cost is not what prevents Leap Motion from achieving the level of robustness required for a competitive end user experience. No low effort memes, gifs, image macros, etc.Read the FAQ before posting a question.This is a place for friendly VR discussion, don’t start drama, attack, or bait other redditors.Welcome to /r/Oculus, a place for Oculus fans to discuss VR.

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