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M.Sc. Alexey Nekrasov
Room 127
Email: nekrasov@vision.rwth-aachen.de

[GitHub]   [Personal Website]   [Office Hours]



Publications


UGainS: Uncertainty Guided Anomaly Segmentation


Alexey Nekrasov, Alexander Hermans, Lars Kuhnert, Bastian Leibe
DAGM German Conference on Pattern Recognition (GCPR) 2023
pubimg

A single unexpected object on the road can cause an accident or may lead to injuries. To prevent this, we need a reliable mechanism for finding anomalous objects on the road. This task, called anomaly segmentation, can be a stepping stone to safe and reliable autonomous driving. Current approaches tackle anomaly segmentation by assigning an anomaly score to each pixel and by grouping anomalous regions using simple heuristics. However, pixel grouping is a limiting factor when it comes to evaluating the segmentation performance of individual anomalous objects. To address the issue of grouping multiple anomaly instances into one, we propose an approach that produces accurate anomaly instance masks. Our approach centers on an out-of-distribution segmentation model for identifying uncertain regions and a strong generalist segmentation model for anomaly instances segmentation. We investigate ways to use uncertain regions to guide such a segmentation model to perform segmentation of anomalous instances. By incorporating strong object priors from a generalist model we additionally improve the per-pixel anomaly segmentation performance. Our approach outperforms current pixel-level anomaly segmentation methods, achieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the RoadAnomaly validation sets respectively.

» Show BibTeX

```
@inproceedings{nekrasov2023ugains,
title = {{UGainS: Uncertainty Guided Anomaly Instance Segmentation}},
author = {Nekrasov, Alexey and Hermans, Alexander and Kuhnert, Lars and Leibe, Bastian},
booktitle = {GCPR},
year = {2023}
}
```





Mix3D: Out-of-Context Data Augmentation for 3D Scenes


Alexey Nekrasov*, Jonas Schult*, Or Litany, Bastian Leibe, Francis Engelmann
International Conference on 3D Vision (3DV) 2021 (Oral)
pubimg

Mix3D is a data augmentation technique for segmenting large-scale 3D scenes. Since scene context helps reasoning about object semantics, current works focus on models with large capacity and receptive fields that can fully capture the global context of an input 3D scene. However, strong contextual priors can have detrimental implications like mistaking a pedestrian crossing the street for a car. In this work, we focus on the importance of balancing global scene context and local geometry, with the goal of generalizing beyond the contextual priors in the training set. In particular, we propose a "mixing" technique which creates new training samples by combining two augmented scenes. By doing so, object instances are implicitly placed into novel out-of-context environments and therefore making it harder for models to rely on scene context alone, and instead infer semantics from local structure as well.

In the paper, we perform detailed analysis to understand the importance of global context, local structures and the effect of mixing scenes. In experiments, we show that models trained with Mix3D profit from a significant performance boost on indoor (ScanNet, S3DIS) and outdoor datasets (SemanticKITTI). Mix3D can be trivially used with any existing method, e.g., trained with Mix3D, MinkowskiNet outperforms all prior state-of-the-art methods by a significant margin on the ScanNet test benchmark 78.1 mIoU.

» Show BibTeX

@inproceedings{Nekrasov213DV,
title = {{Mix3D: Out-of-Context Data Augmentation for 3D Scenes}},
author = {Nekrasov, Alexey and Schult, Jonas and Or, Litany and Leibe, Bastian and Engelmann, Francis},
booktitle = {{International Conference on 3D Vision (3DV)}},
year = {2021}
}





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