Configure a RealityCapture Workstation
Tower system optimized for the best performance in RealityCapture with room to expand
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Case Study with Utah State
Utah State University is using photogrammetry and virtual reality to design a new community on Powder Mountain in the Ogden Valley of Utah. Benjamin George, a professor of landscape architecture at USU, is using three Puget Systems workstations for this particular project, and says by using VR his students are able to design as if they are actually in the landscape. One of the Puget systems runs Pix4D for processing the thousands of images they took of the mountain, while the other two are used for design and modeling in VR.
Imagine being able to close your eyes and visualize what you want to create. With Puget Systems workstations, the students at Utah State are now able to work on projects that exceed what they were able to do before and do it faster.
Why Choose Puget Systems?
Rather than getting a generic workstation, our systems are designed around your unique workflow and are optimized for the work you do every day.
By keeping inventory of our most popular parts, and maintaining a short supply line to parts we need, we are able to offer an industry leading ship time of 7-10 business days on nearly all our system orders.
We make sure our representatives are as accessible as possible, by phone and email. At Puget Systems, you can actually talk to a real person!
Click here for even more reasons!
RealityCapture Workstation FAQ
Q: Is the CPU or GPU (video card) more important for RealityCapture?
A: Like most Photogrammetry applications, a blend of CPU and GPU processing power is needed to get the best results in RealityCapture. We have found that the CPU has a bigger impact overall, and selecting the right model will save you money as well as give you great performance. On the video card side, a NVIDIA GPU is required in order to support CUDA - but we observed a fairly small difference between mid-range to high-end models. You can read more about both CPU and GPU performance in the articles linked to on the right.
Q: How many video cards does RealityCapture support?
A: There is a nice increase in performance for having dual GPUs in RealityCapture: 5-13% faster performance compared to a single card of the same model. We have only tested with up to two video cards, but it is entirely possible that it could scale further. However, using more than two GPUs would require a larger chassis and power supply, and to have three or more cards each operating at PCI-E x8 or x16 would mean moving to a different processor platform as well - which would both increase price and lower performance more than the gain from a third card could offer. Because of that, we recommend sticking with one or two video cards for this application.
Q: How much RAM do I need?
A: Memory requirements in RealityCapture are actually fairly modest, compared to other photogrammetry applications. This program was designed to use "out-of-core" algorithms, which do not require all of the data being worked on to be stored in the main system memory at same time. As such, very large projects can be handled with modest amounts of RAM - but we still recommend 32GB as a minimum, since Windows and other programs running in the background also need some memory space. We also found that to be a solid starting point in our testing.
The developers of this software have provided some additional guidance regarding memory:
"All processing steps except alignment are out of core. RealityCapture will use all available RAM if it leads to a faster computation. Otherwise, it splits jobs so that it fills into the computer RAM. So technically 16GB is enough for reconstruction, texturing, etc - but more RAM could lead to a faster processing.
Memory consumption during the alignment phase depends on the number of images (not size) and the number of detected features per image. For the default setting of 40K features per image, you can expect the following boundaries:
- 2,000 images - 16GB RAM
- 4,000 images - 32GB RAM
- 8,000 images - 64GB RAM
- 16,000 images - 128GB RAM
By decreasing the number of detected features to half you can approximately decrease the memory consumption by half as well. The approximate formula is: RAM = features x images x 200 bytes."
For those who do need to work with really large image sets, our recommended system supports up to 128GB of memory.
Q: Should I get a solid state drive (SSD) or hard drive (HDD)?
A: We strongly recommend using solid-state drives on all computers these days. They have a huge impact on every aspect of computer usage, from faster boot times to more responsive operation. RealityCapture will also load images more quickly from a fast drive, and because of its out-of-core design it also reads and writes data from the drive throughout processing. Image sets can also take up a lot of space, though, so having a secondary hard drive for archival of projects and other data may be helpful.