Nikita Araslanov

I'm a postdoc in the Computer Vision Group at TU Munich in Germany. My research focuses on visual scene understanding with limited supervision. Previously, I was a PhD student working at the Visual Inference Lab in TU Darmstadt. I obtained my Master's in Computer Science from University of Bonn. I also hold a Diploma degree in Mechanical Engineering from Moscow Aviation Institute (specialising in jets).

03/07/2024 Introducing DiffCD: an SDF-based shape reconstruction approach from point clouds. To be presented at ECCV '24!
27/02/2024 Our work on segmentation with hierarchical labels has been accepted at CVPR '24.
16/01/2024 Our work on direct image alignment has been accepted at ICLR '24 (oral presentation, top 1.2%).
20/10/2023 We will present a self-supervised approach for event data at WACV '24.

Lecture

(WS24/25) Computer Vision 3: Segmentation, Detection and Tracking

Course details

Practical Course

(SS24) Geometric Scene Understanding

Course page

	CARLA Drone: Monocular 3D Object Detection from a Different Perspective Johannes Meier, Luca Scalerandi, Oussema Dhaouadi, Jacques Kaiser, Nikita Araslanov and Daniel Cremers DAGM German Conference on Pattern Recognition (GCPR), 2024 (oral) Paper (arXiv) \| Project webpage
	DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting Linus Härenstam-Nielsen, Lu Sang, Abhishek Saroha, Nikita Araslanov and Daniel Cremers European Conference on Computer Vision (ECCV), 2024 Paper (arXiv) \| Code
	Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer and Stefan Roth Transactions on Machine Learning Research (TMLR), 2024 Paper \| Code
	Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball Simon Weber, Barış Zöngür, Nikita Araslanov and Daniel Cremers Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Paper (arXiv) \| Code
	An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment Sergei Solonets^, Daniil Sinitsyn^, Lukas Von Stumberg, Nikita Araslanov and Daniel Cremers International Conference on Learning Representations (ICLR), 2024 (oral) Paper \| Code
	Masked Event Modeling: Self-Supervised Pretraining for Event Cameras Simon Klenk^, David Bonello^, Lukas Koestler^, Nikita Araslanov* and Daniel Cremers Winter Conference on Applications of Computer Vision (WACV), 2024 Paper (arXiv) \| Code
	Semantic Self-adaptation: Enhancing Generalization with a Single Sample Sherwin Bahmani, Oliver Hahn, Eduard Zamfir, Nikita Araslanov, Daniel Cremers and Stefan Roth Transactions on Machine Learning Research (TMLR), 2023 Paper \| Code
	Dense Unsupervised Learning for Video Segmentation Nikita Araslanov, Simone Schaub-Meyer and Stefan Roth Advances in Neural Information Processing Systems (NeurIPS), 2021 Paper \| Supplemental \| Code
	Self-supervised Augmentation Consistency for Adapting Semantic Segmentation Nikita Araslanov and Stefan Roth Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Paper \| Supplemental \| Code
	Single-Stage Semantic Segmentation from Image Labels Nikita Araslanov and Stefan Roth Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Paper \| Supplemental \| Code

	CARLA Drone: Monocular 3D Object Detection from a Different Perspective Johannes Meier, Luca Scalerandi, Oussema Dhaouadi, Jacques Kaiser, Nikita Araslanov and Daniel Cremers DAGM German Conference on Pattern Recognition (GCPR), 2024 (oral) Paper (arXiv) \| Project webpage
	DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting Linus Härenstam-Nielsen, Lu Sang, Abhishek Saroha, Nikita Araslanov and Daniel Cremers European Conference on Computer Vision (ECCV), 2024 Paper (arXiv) \| Code
	Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer and Stefan Roth Transactions on Machine Learning Research (TMLR), 2024 Paper \| Code
	Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball Simon Weber, Barış Zöngür, Nikita Araslanov and Daniel Cremers Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Paper (arXiv) \| Code
	An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment Sergei Solonets^, Daniil Sinitsyn^, Lukas Von Stumberg, Nikita Araslanov and Daniel Cremers International Conference on Learning Representations (ICLR), 2024 (oral) Paper \| Code
	Masked Event Modeling: Self-Supervised Pretraining for Event Cameras Simon Klenk^, David Bonello^, Lukas Koestler^, Nikita Araslanov* and Daniel Cremers Winter Conference on Applications of Computer Vision (WACV), 2024 Paper (arXiv) \| Code
	Semantic Self-adaptation: Enhancing Generalization with a Single Sample Sherwin Bahmani, Oliver Hahn, Eduard Zamfir, Nikita Araslanov, Daniel Cremers and Stefan Roth Transactions on Machine Learning Research (TMLR), 2023 Paper \| Code
	Dense Unsupervised Learning for Video Segmentation Nikita Araslanov, Simone Schaub-Meyer and Stefan Roth Advances in Neural Information Processing Systems (NeurIPS), 2021 Paper \| Supplemental \| Code
	Self-supervised Augmentation Consistency for Adapting Semantic Segmentation Nikita Araslanov and Stefan Roth Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Paper \| Supplemental \| Code
	Single-Stage Semantic Segmentation from Image Labels Nikita Araslanov and Stefan Roth Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Paper \| Supplemental \| Code

About

News

Teaching

Publications (Google Scholar )

CARLA Drone: Monocular 3D Object Detection from a Different Perspective

DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting

Boosting Unsupervised Semantic Segmentation
with Principal Mask Proposals

Flattening the Parent Bias:
Hierarchical Semantic Segmentation in the Poincaré Ball

An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment

Masked Event Modeling: Self-Supervised Pretraining for Event Cameras

Semantic Self-adaptation:
Enhancing Generalization with a Single Sample

Dense Unsupervised Learning for Video Segmentation

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation

Single-Stage Semantic Segmentation from Image Labels