I work on Time-of-flight camera's that need to handle the kind of data that you're referring too.
Each pixel takes a multiple measurements over time of the intensity of reflected light that matches the emission pulse encodings. The result is essentially a vector of intensity over a set of distances.
A low depth resolution example of reflected intensity by time (distance):
i: _ _ ^ _ ^ - _ _
d: 0 1 2 3 4 5 6 7
In the above example, the pixel would exhibit an ambiguity between distances of 2 and 4.
The simplest solution is to select the weighted average or median distance, which results in "flying pixels" or "mixed pixels" for which there are existing efficient techniques for filtration. The bottom line is that for applications like low-latency obstacle detection on a cost-constrained mobile robot, there's some compression of depth information required.
For the sake of inferring a highly realistic model from an image, Neural radiance fields or gaussian splats may best generate the representation that you might be envisioning, where there would be a volumetric representation of material properties like hair. This comes with higher compute costs however and doesn't factor in semantic interpretation of a scene. The Top performing results in photogrammetry have tended to use a combination of less expensive techniques like this one to better handle sparsity of scene coverage, and then refining the a result using more expensive techniques [1].
It’s definitely price performant at 6.5 on the mohs scale, sapphire panels are 2-4x more expensive to produce but 9 on the mohs scales so they’re typically only found in watches I bet. At a 10 on the mohs scale diamond is still best for hardness. Maybe the same effect of the prince Rupert’s drop could be induced on a round diamond with an alpha particle beam treatment where you add these ions primarily on the surface of the stone.
I'm not exactly a watch nerd, so don't know how true this is, but I hear that sapphire shatters on impact. Or maybe that's just an excuse for people peddling "alternative" materials for their watches?
But if it is true, then I'd say that sapphire is a poor choice for mobile phones. IME I'm far more likely to drop the phone, preferably on some form of hard surface, that to scratch it. Phones are nowadays so ridiculously big, that there's zero chance I'll have anything else in my pocket.
Diamonds also shatter. It's got roughly the same toughness as sapphire but the cleavage planes are more annoying (in that it has them, while sapphire doesn't).
They did however manage to get Sapphire on the watch display (at least on the stainless steel LTE edition of the apple watch series 4 and 8, which I'm aware of).
The difference between my watch (which was 4 years old at the time) and a colleagues almost brand new apple watch (without the sapphire) was really telling, is his was lightly scratched on the edges despite him being quite careful, and I was exceedingly reckless with mine as I considered it old- yet there was not a single defect.
That was a major reason for me to upgrade to the LTE of the next version of apple watch that I bought.
I have now had watches for 10 years without issue and my phone displays seem to scratch.
I’m not doubting you’re correct, I never boight it for this reason, it was just an interesting retrospective observation. Clearly its working for me, and I’m not sure I have anything with gorilla glass.
Unless the Pixel 7 has it.
EDIT: My Pixel 7 does have gorilla glass indeed and it has two scratches on the display despite being extremely rarely used. (its my work phone and sits on my desk mostly)
I have a Series 5 with the Saphire glass but my clumsy ass dropped it in the kitchen onto a stone floor. The edge of the glass shattered and while I still use it to this day, it's an ugly reminder of my clumsiness. The repair offer from Apple was to get a new one with a very slight discount.
Long story short, it is more scratch resistant but also more prone to break. So be careful.
I wonder if they could put in tiny reaction wheel gyroscopes (to make it land in a safe orientation) or tiny linear reaction masses, or perhaps tiny replacable airbags or tiny sodium azide NaN3 charges/nozzles to blow air just before landing, to make for a gentler landing, replacable of course.
Tiny replacable airbags, on the corners. Preferably it should somehow detect the elasticity of the surface its falling to or going to hit.
There was a prototype design that a German student, Philip Frenzel, produced in 2018 that used the accelerometers to detect freefall and push out little spring claws to protect the device. It was supposed to be in production circa 2019 I think but I can't see evidence that it made it. YouTube has videos.
I suspect the edges are more at risk than the face, as I've struck the face very hard against the edge of a scaffolding pole and there was no damage - which, actually shocked me a lot based on the force.
The threat isn't so much scratching it in your pocket (but that is possible, I typically have my sunglass clip in the same pocket--it's supposed to be in it's own case but in theory something could happen) as scratching it against things.
For conferences and trade-shows where there is an expectation to network. My company gives us conventional cards with little qr code v-cards.
I only needed a handful of cards for a recent event as I'm not one of the sales engineers. When I realized I wouldn't have any in time if I requested them, I saw it as an opportunity to understand the v-card format and qr codes and write https://github.com/Stedag/qr_card which generated them and I just cut a out a couple sheets of card stock.
My favorite part is that I could set the note for my contact in their phone to be specific to the event while not being sneaky:
Each pixel takes a multiple measurements over time of the intensity of reflected light that matches the emission pulse encodings. The result is essentially a vector of intensity over a set of distances.
A low depth resolution example of reflected intensity by time (distance):
i: _ _ ^ _ ^ - _ _ d: 0 1 2 3 4 5 6 7
In the above example, the pixel would exhibit an ambiguity between distances of 2 and 4.
The simplest solution is to select the weighted average or median distance, which results in "flying pixels" or "mixed pixels" for which there are existing efficient techniques for filtration. The bottom line is that for applications like low-latency obstacle detection on a cost-constrained mobile robot, there's some compression of depth information required.
For the sake of inferring a highly realistic model from an image, Neural radiance fields or gaussian splats may best generate the representation that you might be envisioning, where there would be a volumetric representation of material properties like hair. This comes with higher compute costs however and doesn't factor in semantic interpretation of a scene. The Top performing results in photogrammetry have tended to use a combination of less expensive techniques like this one to better handle sparsity of scene coverage, and then refining the a result using more expensive techniques [1].
1: https://arxiv.org/pdf/2404.08252