Pix4D 4.3: Intel Core i9 9990XE Performance

Always look at the date when you read an article. Some of the content in this article is most likely out of date, as it was written on February 12, 2019. For newer information, see our more recent articles.

Table of Contents

Introduction

Pix4D is an advanced photogrammetry application, suited to a wide range of uses, with a focus on handling images captured by drone cameras. Processing of those images into point clouds and 3D meshes/textures is time-consuming, making heavy use of both the central processing unit (CPU) and graphics processor (GPU / video card) in a computer.

Both core count and clock speed play a role in Pix4D performance, so when Intel released their new Core i9 9990XE with 14 cores running at high clock speeds this seemed like a good way to test it.

Test Hardware

The Core i9 9990XE is part of the Intel's Core X series of processors, and as such uses the same X299 chipset and motherboards. For comparison, we also included results from our previous round of testing that include other Core and Core X chips from Intel as well as a few Ryzen and Threadripper models from AMD.

We used the same video card and memory type/speed throughout since that is what all of these are rated for when using the maximum amount of RAM they support. Some platforms may support higher memory speeds when only one or two memory modules are used, but since RAM capacity is very important for photogrammetry, and sticking with manufacturer-supported speeds is important for reliability, that is what we have prioritized here. If you would like to see full details of the hardware we tested, click here to expand a chart below.

Testing Hardware
Motherboard:	Gigabyte Z370 AORUS Gaming 5	Gigabyte X470 AORUS Gaming 7 WiFi	Gigabyte X299 Designare EX	MSI MEG X399 Creation
CPU:	Intel Core i7 8700K 6 Core 3.7GHz (4.7GHz Turbo) Intel Core i7 9700K 8 Core 3.6GHz (4.9GHz Turbo) Intel Core i9 9900K 8 Core 3.6GHz (5.0GHz Turbo)	AMD Ryzen 2700X 8 Core 3.7GHz (4.3GHz Turbo)	Intel Core i7 7820X 8 Core 3.6GHz (4.5GHz Turbo) Intel Core i9 7900X 10 Core 3.3GHz (4.5GHz Turbo) Intel Core i9 7980XE 18 Core 2.6GHz (4.4GHz Turbo) Intel Core i7 9800X 8 Core 3.8GHz (4.5GHz Turbo) Intel Core i9 9900X 10 Core 3.5GHz (4.5GHz Turbo) Intel Core i9 9980XE 18 Core 3.0GHz (4.5GHz Turbo) Intel Core i9 9990XE 14 Core 4.0GHz (5.1GHz Turbo)	AMD TR 2950X 16 Core 3.5GHz (4.4GHz Turbo) AMD TR 2990X 32 Core 3.0GHz (4.2GHz Turbo)
RAM:	4x Crucial DDR4-2666 16GB (64GB total)		8x Crucial DDR4-2666 16GB (128GB total)
GPU:	NVIDIA GeForce RTX 2080 8GB
Drive:	Samsung 960 Pro M.2 PCI-E x4 NVMe SSD
OS:	Windows 10 Pro 64-bit
Software:	Pix4D 4.3.27*
Note:	*Version 4.4 has some preview releases now, but we are waiting for a full release before switching to it

Methodology

In order to be able to run multiple image sets and multiple iterations, we put together an AutoIt script that runs Pix4D without manual input. Because of the automation we used, all steps were performed back-to-back with no editing in-between to clean up point clouds. That means these results may not perfectly match up with what you'd see when using Pix4D in a real-world workflow, but it removes any chance of human error altering results between runs. We did still observe small variances on total processing time with each image set, so we ran them three times and selected the lowest overall result for each image set to be included in the charts below.

As mentioned above, we tested several different image sets: two each in both 3D Model and 3D Map modes. These are the most demanding of the processing methods in Pix4D, hence our focus on them over the various other options available. No settings were altered from the defaults. Descriptions of the image sets we used, and what processing mode they were used with, are available below.

Image Sets

Here is information about the image sets we used, including project type (3D Model vs 3D Map), and listed in order of complexity:

Eagle Model
44 Images from Pix4D website

Building Map
36 Images from Pix4D website

Wolf Model
100 Images provided by Pix4D

Station Map
739 Images provided by Pix4D

Results

Here are the results for total processing time on each of the four image sets, across all of the tested CPUs. AMD processors are shown in red, older Intel models in light blue, and the new Core i9 99980XE in dark blue. Cycle through the charts via the left and right arrows.

Analysis

Intel's Core i9 9990XE took two top spots, on both the small (44 images) and medium (100 images) models. It fell behind a little on the maps, in particular, our biggest one (739 images). That would be an admirable performance if it weren't for the price and extremely limited availability of this processor. For very small projects, the Core i9 9900K wasn't all that far behind and costs less than a quarter of what the 9990XE does (plus it runs on a less costly chipset/platform overall). For bigger projects, the more widely available and less expensive Core i9 9980XE – with more cores but lower clock speeds – comes quite close, or even bests it when projects get larger. And AMD's Threadripper 2990WX is both faster and more affordable than either of those Intel models when it comes to large image sets!

If you would like a more in-depth look at what makes different CPUs better or worse as image count and project type changes, we dug into that topic a bit further in the analysis section of our last Pix4D CPU performance roundup.

Conclusion

Pix4D is sensitive to both clock speed and core count, but as the number of images in your project increases so does the importance of core count. The new Core i9 9990XE's high clock speed helps a lot, topping performance with small image sets, but still falling behind higher core count models when projects get bigger – especially with maps (versus models). If you work exclusively with small to medium size models and can get your hands on one, the 9990XE won't disappoint – but for larger projects, you can get better performance for less money with either Intel's own Core i9 9980XE or AMD's Threadripper 2990WX, both of which are also much more widely available.