IAM

Check out our CVPR'18 paper on weakly-supervised 3D shape completion — and let me know your opinion! @david_stutz

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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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CVPR’18 Weakly-Supervised Shape Completion Code Released

Finally, we are able to release the code and the data corresponding to our CVPR’18 paper on “Learning 3D Shape Completion from Laser Scan Data with Weak Supervision”. In this article, I want to briefly outline the released code and data.

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Compiling OpenCV 2.4.x with CUDA 9

Currently, both OpenCV 2 and OpenCV 3 seem to have some minor issues with CUDA 9. However, CUDA 9 is required for the latest generation of NVidia graphics cards. In this article, based on this StackOverflow question, I want to discuss a very simple patch to get OpenCV 2 running with CUDA 9.

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

In this CVPR’18 paper, based on my master thesis, we propose a weakly-supervised and learning-based approach to 3D shape completion of sparse and noisy point clouds. We show that, using a learned shape prior, shape completion can be learned without access to ground truth shapes — only by knowing the object category at hand. This article provides the paper and its supplementary material.

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Master Thesis “Learning Shape Completion from Bounding Boxes using CAD Shape Priors”

My master thesis, written at the Autonomous Vision Group of Max Planck Institute for Intelligent Systems under the supervision of Prof. Andreas Geiger, addresses the problem of 3D shape completion of sparse point clouds under weak supervision. Specifically, based on a learned shape prior it is possible to learn 3D shape completion without access to ground truth shapes, as shown on KITTI. This article briefly introduces the problem and the main contributions and offers the thesis as download.

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What I Learned about PhD Programs

In the last few months, I started to pursue a PhD. Although I did not have many options, also because I decided not to apply to many programs, I found choosing the right PhD incredible difficult. In this article, I want to share some of my insights.

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An Up-to-Date List of Superpixel Algorithms

Recently, I started reviewing for different conferences and journals. Based on my previous work, specifically the Superpixel Benchmark, I mostly reviewed papers on superpixel algorithms. For most of these algorithms, and additional related work, I made notes; in this article I want to keep track of the algorithms and benchmarks. The goal is to have an up-to-date list of superpixel algorithms and their implementations.

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Updated CMSimple Plugins and Demo Installations

In the last few days I took some time to revisit some of my CMSimple plugins: News, a news article and blog plugin, Pictures, an image gallery plugin, YouTube, a plugin to create YouTube galleries, and BBClone, a plugin for web analytics with BBClone. In this article, I want to present the main features, the updated documentation as well as two demo applications to try out the plugins.

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Master Thesis Proposal “Shape Completion from Bounding Boxes using CAD Shape Priors”

Part of my master thesis at the Max Planck Institute for Intelligent Systems was an initial proposal — outlining the general idea and the current state-of-the-art. Specifically, I worked on learning 3D shape completion on KITTI using 3D bounding boxes only. In this article, I want to present this proposal.

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KITTI’s 3D Object Detection Benchmark

As part of my stay in the Autonomous Vision Group in Tübingen, I also worked on the KITTI dataset. With the help of Bo Li, we were able to add a novel benchmark: the 3D object detection benchmark. In this article, I briefly want to introduce the benchmark and give some useful hints regarding submission.

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