9 papers
Motion estimation and filtered prediction for dynamic point cloud attribute compression
Haoran Hong, Eduardo Pavez, Antonio Ortega +2
In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-base…
Fractional Motion Estimation for Point Cloud Compression
Haoran Hong, Eduardo Pavez, Antonio Ortega +2
Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes…
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields
Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez +1
This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-call…
Cylindrical coordinates for LiDAR point cloud compression
Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega
We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of s…
Multi-resolution intra-predictive coding of 3D point cloud attributes
Eduardo Pavez, Andre L. Souto, Ricardo L. De Queiroz +1
We propose an intra frame predictive strategy for compression of 3D point cloud attributes. Our approach is integrated with the region adaptive graph Fourier transform (RAGFT), a m…
Spectral folding and two-channel filter-banks on arbitrary graphs
Eduardo Pavez, Benjamin Girault, Antonio Ortega +1
In the past decade, several multi-resolution representation theories for graph signals have been proposed. Bipartite filter-banks stand out as the most natural extension of time do…