From the article:
>Evercoast deployed a 56 camera RGB-D array
Do you know which depth cameras they used?
>Evercoast deployed a 56 camera RGB-D array
Do you know which depth cameras they used?
Can you add any interesting details on the benchmarking done against the RED camera rig?
I assume they stuck with realsense for proper depth maps. However, those are both limited to a 6 meters range, and their depth imaging isn't able to resolve features smaller than their native resolution allows (gets worse after 3m too, as there is less and less parallax among other issues). I wonder how they approached that as well.
So likely RealSense D455.
I recommend asking https://www.linkedin.com/in/benschwartzxr/ for accuracy.
EDIT: I realize a phone is not on the same level as a red camera, but i just saw iphones as a massively cheaper option to alternatives in the field i worked in.
And when I think back to another iconic hip hop (iconic that genre) video where they used practical effects and military helicopters chasing speedboats in the waters off of Santa Monica...I bet they had change to spear.
But yes, you can easily use iPhones for this now.
Check this project, for example: https://zju3dv.github.io/freetimegs/
Unfortunately, these formats are currently closed behind cloud processing so adoption is a rather low.
Before Gaussian splatting, textured mesh caches would be used for volumetric video (e.g. Alembic geometry).
https://developer.apple.com/documentation/spatial/
Edit: As I'm digging, this seems to be focused on stereoscopic video as opposed to actual point clouds. It appears applications like cinematic mode use a monocular depth map, and their lidar outputs raw point cloud data.
You just can't see the back of a thing by knowing the shape of the front side with current technologies.
The depth map stored for image processing is image metadata, meaning it calculates one depth per pixel from a single position in space. Note that it doesn't have the ability to measure that many depth values, so it measures what it can using LIDAR and focus information and estimates the rest.
On the other hand, a point cloud is not image data. It isn't necessarily taken from a single position, in theory the device could be moved around to capture addition angles, and the result is a sparse point cloud of depth measurements. Also, raw point cloud data doesn't necessarily come tagged with point metadata such as color.
I also note that these distinctions start to vanish when dealing with video or using more than one capture device.
So you have to have minimum two for front and back of a dancer. Actually, the seams are kind of dubious so let's say three 120 degrees apart. Well we need ones looking down as well as up for baggy clothing, so more like nine, 30 degrees apart vertically and 120 degrees horizontally, ...
and ^ this will go far down enough that installing few dozens of identical non-Apple cameras in a monstrous sci-fi cage starts making a lot more sense than an iPhone, for a video.
There are usually six sides on a cube, which means you need minimum six iPhone around an object to capture all sides of it to be able to then freely move around it. You might as well seek open-source alternatives than relying on Apple surprise boxes for that.
In cases where your subject would be static, such as it being a building, then you can wave around a single iPhone for the same effect for a result comparable to more expensive rigs, of course.
The standard pipeline can infer a huge amount of data, and there are a few AI tools now for hallucinating missing geometry and backfaces based on context recognition, which can then be converted back into a splat for fast, smooth rendering.