On top of that, our users will have a much more fine-grained control over the processing allowing them to, for example, automatically validate the quality of the calibration before starting the densification.

With the new Python-based engine we are addressing many of the pain points reported by our users by making it very easy to build complex processing pipelines. As a result, several clients had issues during the installation phase which had to be resolved by our support team. While it obviously offered a lot of benefit to our customers, it did require a very good knowledge of Pix4Dmapper for developers to integrate. The engine server product we have been shipping up to now offered a lighter version of Pix4Dmapper. What are the biggest changes in the new Pix4Dengine release? Our future plans include further improving Pix4Dengine based on the feedback from customers and completing some of the new features that we have in the pipeline. Since I started the team at the beginning of this year, the team has grown to seven people which allowed us to complete the first release of the SDK in a very short period of time. Within Pix4D I started working on our algorithms to create 3D point clouds from images, improving their quality as well as reducing the processing time.Īpart from a brief time in our DevOps team, which was a fascinating technical challenge although ultimately not for me, I have continued driving the development of a product as I had been doing at CERN.Īfter some discussion with our CEO, Christoph Strecha, it became clear that there was an urgent need to further strengthen our enterprise offering by taking our engine server product to the next level. This made the switch to Pix4D very natural. Photo courtesy of CERN.Īlthough muon identification is a rather exotic topic, it turned out to be quite closely related to 3D computer vision.

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