Vision4D has been designed to enable scientists to work on images of virtually unlimited size. As long as you have enough hard disk space to hold the data, all functions of Vision4D are available regardless of the size of the dataset. However, processing more data will take more time.

Regarding the 4D viewer performance, the main limiting factor is the graphics card's capabilities. Specifically, the GPU is limited by the amount of video memory available (VRAM), and the speed/number of graphics processing cores.

When the 4D viewer is activated for the first time on a new installation, the software will run a system check to optimize the performance based on the given hardware. This means that depending on the speed of the GPU we will calculate how large a volume we can render at an acceptable level of performance. For a low-end GPU, like onboard graphics systems, this may mean a maximum of around 256 x 256 x 256 pixels. For high-end GPUs, we may be able to render up to 2000 x 2000 x 2000 pixels.

If the dataset is larger than what the GPU can handle smoothly, Vision4D will automatically subsample the image to a more practicable volume. This is why we see a "loading data" progress bar at the bottom of the viewer:

The other indication that we are subsampling is the Resolution values in the status bar. In our example above, the subsampled resolution is 1505x2090x306, which represents 17% of the actual image data.

While working with the 4D viewer, interactivity is clearly a very important factor. This means that we need to be able to render the volume speedily to enable a high frame rate to reduce lag. To enable speedy renders, Vision4D will usually temporarily drop the image data resolution while the volume is moving/rotating, and then enable a higher resolution once the volume is immobile.

The drop in resolution is considered an acceptable trade-off for the speed increase that enables interactivity.

All this means that under normal circumstances, users can always smoothly visualize and navigate through even very large datasets on any computer they might be using the software on.