Nouveaux Contenus Média

11.07.2023

TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM

Most classical SLAM systems rely on the static scene assumption, which limits their applicability in real world scenarios. Recent SLAM frameworks have been proposed to simultaneously track the camera and moving objects. However they are often unable to estimate the canonical pose of the objects and exhibit a low object tracking accuracy. To solve this problem we propose TwistSLAM++, a semantic,…

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15.01.2024

Spatial Rate Allocation for Learning-based Video Coding

This paper presents a method that enables arbitrary end-to-end Learning-based image/video codecs to apply spatial rate allocation. At the frame-level, the forward pass of the underlying encoder network is followed by a latent refinement step, in which a customized loss function is minimized. This loss function takes as input an arbitrary pixel-wise map that defines the interest of each pixel and…

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21.07.2023

Open access dataset of holographic videos for codec analysis and machine learning applications

Despite the growing interest for Holography, there is a lack of publicly available three-dimensional hologram sequences for the evaluation of video codecs with inter-frame compression mechanisms such as motion estimation and compensation. In this paper, we report the first large-scale dataset containing 18 holographic videos computed with three different resolutions and pixel pitches. By…

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09.01.2024

GOP-BASED LATENT REFINEMENT FOR LEARNED VIDEO CODING

This paper presents a method allowing learned video encoders to apply arbitrary latent refinement strategies to serve as Rate-Distortion Optimization (RDO) at the time of encoding. To do so, a latent domain search is applied on an initial latent representation of the video signal. This search is implemented as a set of iterations, each of which performs a gradient descent with back-propagation of…

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