IAM

TAG»C++«

NOVEMBER2022

PROJECT

Several mathematical impage processing exercises implemented in C++ and MatLab.

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NOVEMBER2022

PROJECT

An example of a custom TensorFlow operation implemented in C++.

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NOVEMBER2022

PROJECT

A C++ implementation of density forests.

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NOVEMBER2022

PROJECT

Torch/CUDA implementation of batch normalization for OctNets.

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AUGUST2022

PROJECT

Examples, tools and resources for using Caffe’s Python interface pyCaffe.

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AUGUST2022

PROJECT

3D mesh fusion, voxelization and evaluation for computer vision research.

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ARTICLE

Convolutional Batch Normalization for OctNets

During my master thesis I partly worked on OctNets, octree-bases convolutional neural networks for efficient learning in 3D. Among others, I implemented convolutional batch normalization for OctNets. This article briefly discusses the implementation, which will be available on GitHub.

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ARTICLE

Implementing Torch Modules in C/CUDA

Torch is a framework for scientific computing in LUA. However, it has mostly been used for deep learning research as it provides efficient and comfortable C/CUDA implementations of a wide range of (convolutional and/or recurrent) neural network components. In this article, I want to provide a code template allowing to easily extend torch.nn by custom modules implemented in C and/or CUDA without knowledge of Torch’s core.

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ARTICLE

A C++ Implementation of Mesh-to-Mesh Distance

In 3D vision, a common problem involves the comparison of meshes. In 3D reconstruction or surface reconstruction, triangular meshes are usually compared considering accuracy and completeness — the distance from the reconstruction to the reference and vice-versa. In this article, I want to present an efficient C++ tool for computing accuracy and completeness considering both references meshes as well as reference point clouds.

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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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