IAM

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

TAG»PYTHON«

ARTICLE

Mesh Voxelization into Occupancy Grids and Signed Distance Functions

Triangular meshes are commonly used to represent various shapes in computer graphics and computer vision. However, for various deep learning techniques, triangular meshes are not well suited. Therefore, meshes are commonly voxelized into occupancy grids or signed distance functions. This article presents a C++ tool allowing efficient voxelization of (watertight) meshes.

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ARTICLE

Watertight Meshes by Mesh Fusion

Automatically obtaining high-quality watertight meshes in order to derive well-defined occupancy grids or signed distance functions is a common problem in 3D vision. In this article, I present a mesh fusion approach for obtaining watertight meshes. In combination with a standard mesh simplification algorithm, this approach produces high-quality, but lightweight, watertight meshes.

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ARTICLE

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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19thMAY2018

PROJECT

Learning 3D shape completion under weak supervision; on ShapeNet, ModelNet, KITTI and Kinect data; published at CVPR and on ArXiv.

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ARTICLE

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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ARTICLE

Some TensorFlow Experiments on MNIST

As part of the online course Creative Applications of Deep Learning with TensorFlow, and to get started with TensorFlow, I implemented some experiments on MNIST. Specifically, I tested different architectures, activation functions and initialization schemes. While these experiments are not systematic enough for reliable results, they can be useful as an introduction to TensorFlow. In this article, I want to share the code and the corresponding presentation.

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17thDECEMBER2017

PROJECT

Weakly-supervised shape completion of cars on KITTI using variational auto-encoders; including two synthetic ShapeNet-based benchmark datasets.

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ARTICLE

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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ARTICLE

Implementing Tensorflow Operations in C++ — Including Gradients

In this article, I discuss a simple Tensorflow operation implemented in C++. While the example mostly builds upon the official documentation, it includes trainable parameters and the gradient computation is implemented in C++, as well. As such, the example is slightly more complex compared to the simple ZeroOut operation discussed in the documentation.

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ARTICLE

Setting up Sphinx to Document Python Projects

Sphinx is a Python documentation tool that allows to automatically create clear documentation by parsing Python docstrings. The documentation can further be complemented using reStructuredText — a markup language similar to Markdown. This article gives a brief overview of setting up Sphinx on Ubuntu.

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