IAM

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

PUBLICATIONSBYYEAR

2018
David Stutz, Andreas Geiger.
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[PDF | BibTeX | Project Page]
David Stutz, Alexander Hermans, Bastian Leibe.
Superpixels: an evaluation of the state-of-the-art.
Computer Vision and Image Understanding, Volume 166, 2018.
[DOI | ArXiv | PDF | BibTeX | Project Page]
2016
David Stutz, Alexander Hermans, Bastian Leibe.
Superpixels: an evaluation of the state-of-the-art.
CoRR abs/1612.01601, 2016.
[PDF | BibTeX | Project Page]
2015
David Stutz.
Superpixel segmentation: an evaluation.
German Conference on Pattern Recognition, 2015.
[PDF | BibTeX | Project Page]

RELATEDARTICLESANDPROJECTS

Articles and project pages related to the publications listed above. Also see Projects for an overview as well as THESES and SEMINAR PAPERS .

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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ARTICLE

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

In this CVPR’18 paper, based on my master thesis, we propose a weakly-supervised and learning-based approach to 3D shape completion of sparse and noisy point clouds. We show that, using a learned shape prior, shape completion can be learned without access to ground truth shapes — only by knowing the object category at hand. This article provides the paper and its supplementary material.

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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

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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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

CVIU Paper “Superpixels: An Evaluation of the State-of-the-Art”

In the course of the last couple of semesters, I extended the initial comparison of superpixel algorithms in my bachelor thesis to a comprehensive comparison of 28 state-of-the-art algorithms on 5 datasets with regard to quantitative and qualitative performance. The results are now available on ArXiv.

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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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ARTICLE

GCPR’15 Paper “Superpixel Segmentation: An Evaluation”

After completing my bachelor thesis, I was encouraged to submit the results at the Young Researcher Forum of the German Conference on Pattern Recognition (GCPR) 2015. In this article, I want to share the paper as well as the corresponding poster.

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08thJANUARY2015

PROJECT

A comparison of several state-of-the-art superpixel algorithms using an extended version of the Berkeley Segmentation Benchmark.

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