IAM

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

TAG»C++«

19thMAY2018

PROJECT

Learning 3D shape completion under weak supervision; on ShapeNet, ModelNet, KITTI and Microsoft Kinect data.

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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

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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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

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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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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29thJANUARY2017

PROJECT

Revised C++ implementations of two popular superpixel algorithms, SEEDS and FH, which are shown to outperform the original implementations.

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ARTICLE

Implementation of Felzenszwalb and Huttenlocher’s Graph-Based Image Segmentation

This article presents an implementation of Felzenszwalb and Huttenlocher’s [1] graph-based image segmentation algorithm. The implementation is compared to the original implementation by Felzenszwalb in terms of Boundary Recall, Undersegmentation Error and Explained Variation, as used for evaluating superpixel algorithms. In addition, qualitative results are provided. The implementation is publicly available on GitHub.

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05thDECEMBER2016

PROJECT

A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.

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