10 accepted papers
Recent advances in neural scene representations have transformed immersive multimedia, with 3D Gaussian Splatting (3DGS) enabling real-time photorealistic rendering. Despite its efficiency, 3DGS suffers from large memory requirements and costly training procedures, motivating efforts toward compress…
3D Point Clouds (PCs) have become a valuable tool for representing intricate 3D information. Assessing the quality of PCs remains a challenging task, especially when striving for optimal immersive experiences. This paper introduces a novel metric and training approach that leverages projection-based…
Existing learning-based methods to compress PCs attributes typically employ variational autoencoders (VAE) to learn compact signal representations. However, these schemes suffer from limited reconstruction quality at high bitrates due to their intrinsic lossy nature. More recently, normalizing flows…
Learning-based point cloud (PC) compression is a promising research avenue to reduce the transmission and storage costs for PC applications. Existing learning-based methods to compress PCs have mainly focused on geometry and employ variational autoencoders to learn compact signal representations. Ho…
Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, object…
This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing octree and voxel-based coding. We adaptively partit…
In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep…
Deep generative models have been recently employed to compress images, image residuals or to predict image regions. Based on the observation that state-of-the-art spatial prediction is highly optimized from a rate-distortion point of view, in this work we study how learning-based approaches might be…
RGBD (texture-plus-depth) image representation enriches traditional 2D content with additional geometrical information, having the potential to improve the performance of many computer vision tasks. In image matching, this has been partially studied by considering how depth maps can help render feat…
Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in featu…