no code implementations • ECCV 2020 • Wei Zeng, Sezer Karaoglu, Theo Gevers
Leveraging the layout depth map as an intermediate representation, our proposed method outperforms existing methods for both panorama layout prediction and depth estimation.
no code implementations • 29 Nov 2024 • Xiaoyan Xing, Konrad Groh, Sezer Karaoglu, Theo Gevers, Anand Bhattad
Our approach makes two key contributions: a data curation strategy from the StyleGAN-based relighting model for our training, and a modified diffusion-based ControlNet that processes both latent intrinsic properties from the source image and latent extrinsic properties from the target image.
no code implementations • 4 Nov 2024 • Ruihong Yin, Vladimir Yugay, Yue Li, Sezer Karaoglu, Theo Gevers
To address this, we present a 3D Gaussian-based novel view synthesis method using sparse input images that can accurately render the scene from the viewpoints not covered by the training images.
no code implementations • 16 Sep 2024 • Başak Melis Öcal, Maxim Tatarchenko, Sezer Karaoglu, Theo Gevers
Specifically, RealDiff simulates a diffusion process at the missing object parts while conditioning the generation on the partial input to address the multimodal nature of the task.
no code implementations • 28 Aug 2024 • Ruihong Yin, Yunlu Chen, Sezer Karaoglu, Theo Gevers
To tackle this issue, our work proposes a novel approach for indoor scene reconstruction, which instead parameterizes the density function with the Signed Ray Distance Function (SRDF).
no code implementations • ICCV 2023 • Ruihong Yin, Sezer Karaoglu, Theo Gevers
First, geometry-guided feature learning encodes geometric priors to contain view-dependent information.
no code implementations • 30 Jul 2024 • Başak Melis Öcal, Maxim Tatarchenko, Sezer Karaoglu, Theo Gevers
Designing high-quality indoor 3D scenes is important in many practical applications, such as room planning or game development.
no code implementations • 29 Jul 2024 • Xiaoyan Xing, Vincent Tao Hu, Jan Hendrik Metzen, Konrad Groh, Sezer Karaoglu, Theo Gevers
This paper introduces a novel approach to illumination manipulation in diffusion models, addressing the gap in conditional image generation with a focus on lighting conditions.
1 code implementation • 11 Oct 2023 • Osman Ülger, Yu Wang, Ysbrand Galama, Sezer Karaoglu, Theo Gevers, Martin R. Oswald
Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects.
1 code implementation • 20 Jul 2023 • Xiaoyan Xing, Konrad Groh, Sezer Karaoglu, Theo Gevers
The purpose of intrinsic decomposition is to separate an image into its albedo (reflective properties) and shading components (illumination properties).
1 code implementation • 30 Aug 2022 • Partha Das, Sezer Karaoglu, Arjan Gijsenij, Theo Gevers
An ablation study is conducted showing that the use of the proposed priors and progressive CNN increase the IID performance.
no code implementations • Computer Vision and Image Understanding 2022 • YaHui Zhang, ShaoDi You, Sezer Karaoglu, Theo Gevers
Multi-person 3D pose estimation with absolute depths for a fisheye camera is a challenging task but with valuable applications in daily life, especially for video surveillance.
1 code implementation • CVPR 2022 • Partha Das, Sezer Karaoglu, Theo Gevers
An extensive ablation study and large scale experiments are conducted showing that it is beneficial for edge-driven hybrid IID networks to make use of illumination invariant descriptors and that separating global and local cues helps in improving the performance of the network.
no code implementations • 2 Sep 2021 • Partha Das, Yang Liu, Sezer Karaoglu, Theo Gevers
However, most of the existing color constancy methods are designed for single light sources.
1 code implementation • 9 Nov 2020 • Hoang-An Le, Thomas Mensink, Partha Das, Sezer Karaoglu, Theo Gevers
Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems.
no code implementations • 22 Oct 2020 • Ipek Ganiyusufoglu, L. Minh Ngô, Nedko Savov, Sezer Karaoglu, Theo Gevers
In this paper, we empirically show that existing approaches on image and sequence classifiers generalize poorly to new manipulation techniques.
1 code implementation • 3 Sep 2020 • Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink
In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model.
no code implementations • 3 Sep 2020 • Anil S. Baslamisli, Yang Liu, Sezer Karaoglu, Theo Gevers
We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading).
1 code implementation • ECCV 2020 • Wei Wang, ShaoDi You, Sezer Karaoglu, Theo Gevers
The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.
1 code implementation • 9 Dec 2019 • Anil S. Baslamisli, Partha Das, Hoang-An Le, Sezer Karaoglu, Theo Gevers
The aim is to distinguish strong photometric effects from reflectance variations.
1 code implementation • 29 Oct 2019 • Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink
For the quality of the image reconstruction and disparity prediction, a combination of different losses is used, including L1 image reconstruction losses and left-right disparity smoothness.
no code implementations • 26 Dec 2018 • Minh Ngô, Burak Mandira, Selim Fırat Yılmaz, Ward Heij, Sezer Karaoglu, Henri Bouma, Hamdi Dibeklioglu, Theo Gevers
Lies and deception are common phenomena in society, both in our private and professional lives.
no code implementations • 18 Dec 2018 • Jian Han, Sezer Karaoglu, Hoang-An Le, Theo Gevers
In this paper, we provide a synthetic data generator methodology with fully controlled, multifaceted variations based on a new 3D face dataset (3DU-Face).
no code implementations • 7 Dec 2018 • Partha Das, Anil S. Baslamisli, Yang Liu, Sezer Karaoglu, Theo Gevers
In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs.
1 code implementation • 5 Dec 2018 • Hoàng-Ân Lê, Tushar Nimbhorkar, Thomas Mensink, Anil S. Baslamisli, Sezer Karaoglu, Theo Gevers
There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today.
no code implementations • 4 Dec 2018 • Wei Zeng, Sezer Karaoglu, Theo Gevers
In this paper, we propose a pipeline to generate 3D point cloud of an object from a single-view RGB image.
1 code implementation • ECCV 2018 • Anil S. Baslamisli, Thomas T. Groenestege, Partha Das, Hoang-An Le, Sezer Karaoglu, Theo Gevers
To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation.
no code implementations • 3 May 2015 • Basura Fernando, Sezer Karaoglu, Sajib Kumar Saha
This paper presents a novel multi scale gradient and a corner point based shape descriptors.
no code implementations • 26 Dec 2014 • Sezer Karaoglu, Yang Liu, Theo Gevers
Experiments on the PASCAL VOC07 and VOC10 datasets show that the proposed method significantly outperforms single object detectors, DPM (8. 4%), CN (6. 8%) and EES (17. 0%) on VOC07 and DPM (6. 5%), CN (5. 5%) and EES (16. 2%) on VOC10.