no code implementations • 3 Apr 2024 • Ata Çelen, Guo Han, Konrad Schindler, Luc van Gool, Iro Armeni, Anton Obukhov, Xi Wang
Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality.
no code implementations • 4 Mar 2024 • Yujia Liu, Anton Obukhov, Jan Dirk Wegner, Konrad Schindler
What makes 3D building reconstruction from airborne LiDAR hard is the large diversity of building designs and especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or to the viewing angle of the sensor.
no code implementations • 7 Dec 2023 • Yujia Liu, Anton Obukhov, Jan Dirk Wegner, Konrad Schindler
Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision, graphics, and machine learning; it saves the designer significant time when iterating on in-the-wild objects.
no code implementations • 5 Dec 2023 • Yuru Jia, Lukas Hoyer, Shengyu Huang, Tianfu Wang, Luc van Gool, Konrad Schindler, Anton Obukhov
Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability to generate creative content, specialize to user data through few-shot fine-tuning, and condition their output on other modalities, such as semantic maps.
2 code implementations • 4 Dec 2023 • Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler
Monocular depth estimation is a fundamental computer vision task.
Ranked #5 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
2 code implementations • 15 Sep 2023 • Tianfu Wang, Menelaos Kanakis, Konrad Schindler, Luc van Gool, Anton Obukhov
Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators.
1 code implementation • ICCV 2023 • Suman Saha, Lukas Hoyer, Anton Obukhov, Dengxin Dai, Luc van Gool
EDAPS significantly improves the state-of-the-art performance for panoptic segmentation UDA by a large margin of 20% on SYNTHIA-to-Cityscapes and even 72% on the more challenging SYNTHIA-to-Mapillary Vistas.
Ranked #1 on Domain Adaptation on Panoptic SYNTHIA-to-Mapillary
1 code implementation • 24 Nov 2022 • Alexandra Senderovich, Ekaterina Bulatova, Anton Obukhov, Maxim Rakhuba
We demonstrate the improved properties of modern CNNs with our method and analyze its impact on the model performance, calibration, and adversarial robustness.
no code implementations • ICCV 2023 • Shengqu Cai, Eric Ryan Chan, Songyou Peng, Mohamad Shahbazi, Anton Obukhov, Luc van Gool, Gordon Wetzstein
Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task.
Ranked #1 on Perpetual View Generation on LHQ
1 code implementation • 30 Sep 2022 • Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis, Konrad Schindler, Luc van Gool
Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations.
2 code implementations • 26 Feb 2022 • Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc van Gool
We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object or a scene of a specific class, conditioned on a single input image.
1 code implementation • CVPR 2022 • Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc van Gool
We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image.
1 code implementation • CVPR 2021 • Suman Saha, Anton Obukhov, Danda Pani Paudel, Menelaos Kanakis, Yuhua Chen, Stamatios Georgoulis, Luc van Gool
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
1 code implementation • ICCV 2021 • David Bruggemann, Menelaos Kanakis, Anton Obukhov, Stamatios Georgoulis, Luc van Gool
Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations.
Ranked #78 on Semantic Segmentation on NYU Depth v2
1 code implementation • 7 Mar 2021 • Anton Obukhov, Maxim Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc van Gool
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
1 code implementation • ECCV 2020 • Menelaos Kanakis, David Bruggemann, Suman Saha, Stamatios Georgoulis, Anton Obukhov, Luc van Gool
First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning).
1 code implementation • ICML 2020 • Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc van Gool
Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks.
no code implementations • 11 Jun 2019 • Anton Obukhov, Stamatios Georgoulis, Dengxin Dai, Luc van Gool
State-of-the-art approaches for semantic segmentation rely on deep convolutional neural networks trained on fully annotated datasets, that have been shown to be notoriously expensive to collect, both in terms of time and money.
Image Segmentation Weakly supervised Semantic Segmentation +1