Search Results for author: Maurice Quach

Found 7 papers, 7 papers with code

Lossless Coding of Point Cloud Geometry using a Deep Generative Model

1 code implementation1 Jul 2021 Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel

This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy.

Arithmetic Data Augmentation

Multiscale deep context modeling for lossless point cloud geometry compression

2 code implementations20 Apr 2021 Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel

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 MPEG G-PCC codec.

A deep perceptual metric for 3D point clouds

1 code implementation25 Feb 2021 Maurice Quach, Aladine Chetouani, Giuseppe Valenzise, Frederic Dufaux

In addition, we propose a novel truncated distance field voxel grid representation and find that it leads to sparser latent spaces and loss functions that are more correlated with perceived visual quality compared to a binary representation.

Learning-based lossless compression of 3D point cloud geometry

1 code implementation30 Nov 2020 Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel

On the one hand, octree representation can eliminate the sparsity in the point cloud.

Arithmetic

Improved Deep Point Cloud Geometry Compression

2 code implementations16 Jun 2020 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, cultural heritage, etc.

Autonomous Driving Mixed Reality

Folding-based compression of point cloud attributes

1 code implementation11 Feb 2020 Maurice Quach, Giuseppe Valenzise, Frederic Dufaux

However, as this mapping process is lossy in nature, we propose several strategies to refine it so that attributes can be mapped to the 2D grid with minimal distortion.

Representation Learning

Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression

2 code implementations20 Mar 2019 Maurice Quach, Giuseppe Valenzise, Frederic Dufaux

Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions.

Mixed Reality Quantization

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