Latency Compensation Through Image Warping For Remote Rendering-Based Volumetric Video Streaming

in this work, we suggest Kernel Filtering Linear Overparameterization (KFLO), where a linear cascade of filtering layers is used during training to improve network performance in test time. We implement this cascade in a kernel filtering fashion, which prevents the trained architecture from becoming unnecessarily deeper. This also allows using our approach with almost any network architecture and let combining the filtering layers into a single layer in test time. Thus, our approach does not add computational omplexity during inference. We demonstrate the advantage of KFLO on various network models and datasets in supervised learning.