Efficient lossy and lossless compression of point clouds

Publications

Improving Point Cloud to Surface Reconstruction with Generalized Tikhonov Regularization

Publication . Guarda, André; Bioucas-Dias, José M.; M. M. Rodrigues, Nuno; Pereira, Fernando; Bernardo Pereira, Fernando Manuel

Point cloud rendering has a vital role in the user Quality of Experience for applications adopting point cloud based representations. While this is not a new area, it has recently become more relevant with the recent interest on point cloud coding by major standardization groups, notably JPEG and MPEG. The screened Poisson surface reconstruction is a state-ofthe- art technique for generating a watertight surface mesh from the point cloud samples. While its screening component allows the surface to better fit the cloud points, this fitting may lead to undesired artifacts in the surface, notably when the point cloud is noisy. This paper proposes to improve this reconstruction method by making it more robust to noise by adopting a generalized Tikhonov regularization term. The proposed regularization approach smooths regions that should be flat while keeping the important details in the edges, thus creating more pleasant surface reconstructions.

2017conference paper

Open access

Point Cloud Coding: Adopting a Deep Learningbased Approach

Publication . Guarda, André; M. M. Rodrigues, Nuno; Pereira, Fernando

Point clouds have recently become an important visual representation format, especially for virtual and augmented reality applications, thus making point cloud coding a very hot research topic. Deep learning-based coding methods have recently emerged in the field of image coding with increasing success. These coding solutions take advantage of the ability of convolutional neural networks to extract adaptive features from the images to create a latent representation that can be efficiently coded. In this context, this paper extends the deep-learning coding approach to point cloud coding using an autoencoder network design. Performance results are very promising, showing improvements over the Point Cloud Library codec often taken as benchmark, thus suggesting a significant margin of evolution for this new point cloud coding paradigm.

2019-11Journal article

Open access

Deep Learning-Based Point Cloud Coding: A Behavior and Performance Study

Publication . M. M. Rodrigues, Nuno; Guarda, André; Pereira, Fernando

Point 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 making

2019-10Journal article

Restricted access