IAM

Check out our latest research on adversarial robustness and generalization of deep networks.

TAG»COMPUTER VISION«

ARTICLE

Discussion and Survey of Adversarial Examples and Robustness in Deep Learning

Adversarial examples are test images which have been perturbed slightly to cause misclassification. As these adversarial examples are usually unproblematic for us humans, but are able to easily fool deep neural networks, their discovery has sparked quite some interest in the deep learning and privacy/security communities. In this article, I want to provide a rough overview of the topic including a brief survey of relevant literature and some ideas on future research directions.

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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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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

t-SNE, 3D Vision and Being a Good CVPR Citizen — Notes from CVPR’18

Last week, I attended my very first CVPR in Salt Lake City, where I also presented my work on weakly-supervised 3D shape completion. In the course of the week, I attended several tutorials as well as all oral and poster sessions. In this article, I want to share my notes and some general comments.

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ARTICLE

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

This article presents the poster for our CVPR’18 paper on weakly-supervised 3D shape completion. The poster also includes improved results form our latest ArXiv pre-print. Meet us in Salt Lake City, Tuesday June 18th, to discuss.

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ARTICLE

Qualification Exam “Learning Shape Completion from Bounding Boxes with CAD Shape Priors”

At at the Max Planck Institute for Informatics, PhD students — which are enrolled in the Saarbr├╝cken Graduate School for Computer Science — have to pass a qualification exam. In this article, I want to share the slides of my qualification exam talk.

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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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ARTICLE

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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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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