7 papers
Lossless Coding of Point Cloud Geometry using a Deep Generative Model
Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise +1
This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take in…
Multiscale deep context modeling for lossless point cloud geometry compression
Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise +1
We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MP…
A deep perceptual metric for 3D point clouds
Maurice Quach, Aladine Chetouani, Giuseppe Valenzise +1
Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recen…
Learning-based lossless compression of 3D point cloud geometry
Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise +1
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…
Improved Deep Point Cloud Geometry Compression
Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
Point clouds have been recognized as a crucial data structure for 3D content and are essential in a number of applications such as virtual and mixed reality, autonomous driving, cu…
Folding-based compression of point cloud attributes
Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent adva…