M. M. Rodrigues, NunoGuarda, AndréPereira, Fernando2025-06-162025-06-162019-10Guarda, A. F., Rodrigues, N. M., & Pereira, F. (2019, October). Deep learning-based point cloud coding: A behavior and performance study. In 2019 8th European Workshop on Visual Information Processing (EUVIP) (pp. 34-39). IEEE. doi: 10.1109/EUVIP47703.2019.8946211.978-1-7281-4496-22471-89632164-974Xhttp://hdl.handle.net/10400.8/13255Article number 89462118th European Workshop on Visual Information Processing, EUVIP 2019 Rome28 October 2019through 31 October 2019Code 156571Point clouds are an emerging 3D visual representation model for immersive and interactive multimedia applications, inparticular for virtual and augmented reality. The huge amount of data associated to point clouds critically asks for efficient point cloud coding technology. While there are already some point cloud coding paradigms in the literature, notably octree, patch and graph-based for geometry data, very recently deep learning emerged in this research domain, offering very promising performances for image coding. While deep learning-based methods often provide interesting results, the understanding of this type of coding solutions is essential to improve their design in order to be used effectively. In this context, this paper presents a study and analysis on the behavior and performance of a deep learning-based point cloud coding solution based on an autoencoder network using only convolutional layers. Beside a promising RD performance, other findings should allow makingengThree-dimensional displaysEncodingGeometryImage codingPoint cloud codingConvolutional neural networkjournal article10.1109/euvip47703.2019.8946211