About
Publications
Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Bingxin Ke Anton Obukhov Shengyu Huang Nando Metzger Rodrigo Caye Daudt Konrad Schindler
Preprint, under review
Breathing New Life into 3D Assets with Generative Repainting
Tianfu Wang Menelaos Kanakis Konrad Schindler Luc Van Gool Anton Obukhov
🦄 Oral talk at the British Machine Vision Conference (BMVC) 2023
Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds
Yujia Liu Anton Obukhov Jan Dirk Wegner Konrad Schindler
Preprint, under review
DiffDreamer: Consistent Single-view Perpetual View Generation with Conditional Diffusion Models
International Conference on Computer Vision (ICCV) 2023
Spectral Tensor Train Parameterization of Deep Learning Layers
International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
T-Basis: a Compact Representation for Neural Networks
International Conference on Machine Learning (ICML) 2020
News
- 2023.09: One papers was accepted at BMVC 2023 as Oral: “Breathing New Life into 3D Assets with Generative Repainting.”
- 2023.07: Two papers were accepted at ICCV 2023: “DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models” and “EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation.”
- 2023.04: One paper was accepted at CVPR Structural and Compositional Learning on 3D Data 2023 workshop: “Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds.”
- 2023.03: One paper was accepted at ICLR Neural Fields 2023 workshop: “TT-NF: Tensor Train Neural Fields.”
- 2022.11: One paper was accepted at SlowDNN 2023 workshop: “TT-NF: Tensor Train Neural Fields.”
- 2022.11: Gave an invited talk at Huawei Zurich about our latest work, “TT-NF: Tensor Train Neural Fields.”
- 2022.11: Started as a postdoc in the Photogrammetry and Remote Sensing Group at ETH Zurich with Professor Konrad Schindler.
- 2022.09: Successfully defended my Ph.D. thesis titled “Tensor Decompositions in Deep Learning.”
- 2022.08: One paper was accepted at NeurIPS 2022: “Towards Practical Control of Singular Values of Convolutional Layers.”
- 2022.07: Attended ICVSS summer school 2022.
- 2022.06: Two patents were published based on the AISTATS 2021 paper “Spectral Tensor Train Parameterization of Deep Learning Layers.”
- 2022.03: One paper was accepted at CVPR 2022: “Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation.”
- 2021.12: One patent was published based on the ICML 2020 paper “T-Basis: a Compact Representation for Neural Networks.”
- 2021.07: One paper was accepted at ICCV 2021: “Exploring Relational Context for Multi-Task Dense Prediction.”
- 2021.06: One paper was accepted into Sparsity in Neural Networks 2021 Workshop: “Spectral Tensor Train Parameterization of Deep Learning Layers.”
- 2021.03: One paper was accepted at CVPR 2021: “Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation.”
- 2021.04: torch-fidelity will be featured at PyTorch Ecosystem Day 2021.
- 2021.01: One paper was accepted at AISTATS 2021: “Spectral Tensor Train Parameterization of Deep Learning Layers.”
- 2020.07: One paper was accepted at ECCV 2020: “Reparameterizing Convolutions for Incremental Multi-task Learning without Task Interference”.
- 2020.06: One paper was accepted at ICML 2020: “T-Basis: a Compact Representation for Neural Networks.”
- 2018.05: Started as a Ph.D. candidate in the Computer Vision Lab at ETH Zurich with Professor Luc Van Gool.