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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Invited Talk on Superpixels at the Max Planck Institute for Informatics in Saarbrücken

In April, I visited Prof. Bernt Schiele’s Computer Vision and Multimodal Computing Department at the Max Planck Institute for Informatics in Saarbrücken. Aside from a presentation on my recent superpixel benchmark, I also met many interesting people and learned a lot about a career in research.

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Mission Statements From My PhD Applications

In the last few months I applied to two different PhD programs offered by the Max Planck Institute for Intelligent Systems in Tübingen. In this article, I want to share the mission statements required for the applications.

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Easily Referencing Publications, Listings or Figures using jQuery and WordPress

This article presents a very simple jQuery plugin and the corresponding Wordpress plugin allowing to easily create and reference labels in a LaTeX-style fashion.

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3D Convolutional Neural Networks — A Reading List

Lifting convolutional neural networks to 3D data is challenging due to different data modalities (videos, image volumes, CAD models, LiDAR data etc.) as well as computational limitations (regarding runtime and memory). In this article, I want to summarize several recent papers addressing these problems and tackling different applications such as shape recognition, shape retrieval, medical image segmentation or object detection.

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Notes on Goodfellow’s “Deep Learning” Textbook

In this article, I collect my notes on Ian Goodfellow’s Deep Learning textbook. While the basic chapters are not included, the notes can be understood as short summaries of the corresponding chapters.

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Visualizing Triangular Meshes from .off Files using Python occmodel

This article discusses how to visualize triangular meshes available in Object File Format (.off) in Python using occmodel. Installation instructions for installing occmodel on Ubuntu are included.

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Reviews and Rebuttal for “Superpixels: An Evaluation of the State-of-the-Art”

This article summarizes the reviews corresponding to our paper “Superpixels: An Evaluation of the State-of-the-Art”. The paper was accepted for publication in Computer Vision and Image Understanding. The reviews correspond to v2 on ArXiv. The updated version will be made available on ArXiv.

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A Note on Extending Tensorflow, PyTorch and Theano for Deep Learning on Custom DataStructures

Many recent deep learning frameworks such as Tensorflow, PyTorch, Theano or Torch are based on dense tensors. However, deep learning on non-tensor data structures is also interesting – especially for sparse, three-dimensional data. This article summarizes some of my experiences regarding deep learning on custom data structures in the mentioned libraries.

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Examples for Getting Started with Torch for Deep Learning

This article is a collection of Torch examples meant as introduction to get started with Lua and Torch for deep learning research. The examples can also be considered individually and cover common use cases such as training on CPU and GPU, weight initialization and visualization, custom modules and criteria as well as saving and fine-tuning models.

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Inspecting Tensorflow’s Tensors using C++ and Bazel

Currently it is difficult to successfully link C++ projects with Tensorflow. However, to compile and run smaller code snippets based on Tensorflow, it might be convenient to put the code inside the tensorflow code base and compile an individual executable using Bazel.

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