Earlier, we had written about how WebODM is proving itself to be a solid alternative to Pix4D. In this post, we compare the 2D outputs generated by both to see if WebODM can hold its own against Pix4D.

Dataset

In order to keep the competition fair, we have taken the dataset to be what Pix4D has decided to showcase itself: a demo project from Pix4D. We will be using the AutoGCP Demo Dataset as present on https://cloud.pix4d.com/demo.

Target Area

The dataset is a flat empty field with some roads and little vegetation. There is little grass cover and some small tents have been erected over the field.

Flight Characteristics

Flight Pattern: Lawnmower Grid
Front Overlap: 75%
Side Overlap: 80%
Flight Altitude: 75m / 246 feet AGL
Average GSD: 1.48cm/pixel / 0.58in/pixel
Area Covered: 0.191km² / 47.34 acres

Inputs

Number of images: 638
Input image resolution: 5472×3648
Camera model name: FC6310R_8.8_5472x3648 (RGB)
Drone model name: DJI Phantom 4 Pro

Methodology

We will comparing the outputs processed from WebODM against the outputs already present in the demo project.

Processing Options

Pix4D

Pix4D Version: 4.5.2

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WebODM

ODM version: 2.3.3
orthophoto-resolution: 1.0
dtm: true
dem-resolution: 1.0
ignore-gsd: true
dsm: true

Result

Quality

First, we will look at the quality of the outputs.

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It seems that WebODM does a pretty good job of matching up to Pix4D’s quality. The features appear to be equally crisp and there is no visible distortion. Color-wise the outputs also appear to be similar. Few differences can be noted in both sets of outputs as well. WebODM tends to handle thin vertical features such as Lamp-posts, poles, benches etc. slightly better. WebODM also tends to handle trees better, preserving the definition in branches, while Pix4D’s output gets a bit blurred when it comes to trees. In general it appears that whenever there is a structure obstructing ground, Pix4D emphasizes the ground while WebODM emphasizes the structure. On the other hand, Pix4D is slightly better with handling small features with linear edges such as the yellow road dividers.

Accuracy

In order to compare accuracy, we compare the RMS error for 3 checkpoints: N5, N6 and N18. We did this comparison for two outputs. One, when GCPs were provided and second, when GCPs were not provided.

With GCP

	WebODM – X Error	WebODM – Y Error	WebODM – Z Error	Pix4D – X Error	Pix4D – Y Error	Pix4D – Z Error
N5	-0.0047	-0.0326	-0.121	-0.0069	0.0040	0.0046
N6	0.0001	0.0118	-0.137	0.0151	0.0004	0.0131
N18	0.0013	0.0171	0.063	-0.0171	0.0069	-0.0286
RMS	0.003	0.022	0.112	0.014	0.005	0.018

Even though both WebODM and Pix4D achieve pretty low error margins, Pix4D offers a slightly better accuracy than WebODM in the horizontal while it is quite better when it comes to accuracy in the vertical direction. If your accuracy requirements can tolerate these margins, WebODM might not be a bad choice.

Without GCP

	WebODM – X Error	WebODM – Y Error	WebODM – Z Error	Pix4D – X Error	Pix4D – Y Error	Pix4D – Z Error
N5	0.0118	-0.0748	-25.221	-0.019	-0.005	-24.451
N6	-0.0144	0.0123	-25.217	-0.015	0.003	-24.447
N18	-0.037	0.0124	-25.117	-0.008	0.006	-24.487
RMS	0.049	0.040	25.185	0.015	0.005	24.462

Again, we see pretty close results. Both software were able to achieve pretty similar accuracy in the horizontal direction and were well off the mark in vertical direction but with similar errors.

Conclusion

Therefore we can see that not only does the free and open-source WebODM hold its ground, it is able to head to head with a much older and pricier Pix4D. Perhaps this is a reflection of how far photogrammetric processing has come and the fact that it is not a privately guarded secret anymore. The world is always better with one more alternative.

Managed WebODM Deployment

Due to its open nature of licensing, WebODM allows for powerful cloud deployments which can provide much cheaper and faster processing in an automated fashion than the usual workstation-based deployments. If you have a business requirement for managed WebODM deployments, check out WebODM on Cloud.