A5062530447- Advanced Vision and Imaging
- Computer Graphics and Visualization Techniques
- Image Enhancement Techniques
- Neural Networks and Applications
- Advanced Image and Video Retrieval Techniques
- Image Processing and 3D Reconstruction
- Model Reduction and Neural Networks
- Advanced Image Processing Techniques
- Image Retrieval and Classification Techniques
Max Planck Institute for Informatics
2023-2024
Northwestern Polytechnical University
2021-2024
Image relighting is attracting increasing interest due to its various applications. From a research perspective, im-age can be exploited conduct both image normalization for domain adaptation, and also data augmentation. It has multiple direct uses photo montage aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided challenge.We rely on VIDIT dataset each of our two challenge tracks, including information. The first track one-to-one where goal transform illumination...
Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they hardly amenable to signal processing. As remedy, we present method perform general convolutions with signals such as neural fields. Observing that piecewise polynomial kernels reduce sparse set of Dirac deltas after repeated differentiation, leverage convolution identities and train integral field efficiently execute large-scale...
Existing any-to-any relighting methods suffer from the task-aliasing effects and loss of local details in image generation process, such as shading attached-shadow. In this paper, we present PNRNet, a novel neural architecture that decomposes task into three simpler sub-tasks, i.e. lighting estimation, color temperature transfer, direction to avoid effects. These sub-tasks are easy learn can be trained with direct supervisions independently. To better preserve attached-shadow details,...
Gaussian scale spaces are a cornerstone of signal representation and processing, with applications in filtering, multiscale analysis, anti-aliasing, many more. However, obtaining such space is costly cumbersome, particular for continuous representations as neural fields. We present an efficient lightweight method to learn the fully continuous, anisotropic arbitrary signal. Based on Fourier feature modulation Lipschitz bounding, our approach trained self-supervised, i.e., training does not...
Image harmonization aims at adjusting the appearance of foreground to make it more compatible with background. Without exploring background illumination and its effects on elements, existing works are incapable generating a realistic shading. In this paper, we decompose image task into two sub-problems: 1) estimation 2) re-rendering objects under illumination. Before solving these sub-problems, first learn shading-aware descriptor via well-designed neural rendering framework, which key is...
Gaussian scale spaces are a cornerstone of signal representation and processing, with applications in filtering, multiscale analysis, anti-aliasing, many more. However, obtaining such space is costly cumbersome, particular for continuous representations as neural fields. We present an efficient lightweight method to learn the fully continuous, anisotropic arbitrary signal. Based on Fourier feature modulation Lipschitz bounding, our approach trained self-supervised, i.e., training does not...
Image harmonization aims at adjusting the appearance of foreground to make it more compatible with background. Without exploring background illumination and its effects on elements, existing works are incapable generating a realistic shading. In this paper, we decompose image task into two sub-problems: 1) estimation 2) re-rendering objects under illumination. Before solving these sub-problems, first learn shading-aware descriptor via well-designed neural rendering framework, which key is...