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Check out our latest research on weakly-supervised 3D shape completion.

TAG»DEEP LEARNING«

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t-SNE, 3D Vision and Being a Good CVPR Citizen — Notes from CVPR’18

Last week, I attended my very first CVPR in Salt Lake City, where I also presented my work on weakly-supervised 3D shape completion. In the course of the week, I attended several tutorials as well as all oral and poster sessions. In this article, I want to share my notes and some general comments.

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CVPR’18 Poster “Learning 3D Shape Completion from Laser Scan Data with Weak Supervision”

This article presents the poster for our CVPR’18 paper on weakly-supervised 3D shape completion. The poster also includes improved results form our latest ArXiv pre-print. Meet us in Salt Lake City, Tuesday June 18th, to discuss.

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Qualification Exam “Learning Shape Completion from Bounding Boxes with CAD Shape Priors”

At at the Max Planck Institute for Informatics, PhD students — which are enrolled in the Saarbrücken Graduate School for Computer Science — have to pass a qualification exam. In this article, I want to share the slides of my qualification exam talk.

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ArXiv Pre-Print Improved Weakly-Supervised 3D Shape Completion Code Released

We are releasing the code and data corresponding to our ArXiv pre-print on weakly-supervised 3D shape completion — a follow-up work on our earlier CVPR’18 paper. The article provides links to the GitHub repositories and data downloads as well as detailed descriptions. It also highlights the differences between the two papers.

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ArXiv Pre-Print “Learning 3D Shape Completion under Weak Supervision”

In this follow-up on our CVPR’18 work, we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is now available as pre-print on ArXiv. Abstract, some experimental results and a comparison to our CVPR’18 work can be found in this article.

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