| Titre : |
3D point cloud compression |
| Type de document : |
Thèse/HDR |
| Auteurs : |
Chao Cao, Auteur ; Titus Zaharia, Directeur de thèse ; Marius Preda, Directeur de thèse |
| Editeur : |
Paris : Institut Polytechnique de Paris |
| Année de publication : |
2021 |
| Importance : |
165 p. |
| Format : |
21 x 30 cm |
| Note générale : |
Bibliographie
Thèse de doctorat de l’Institut polytechnique de Paris, Spécialité Informatique |
| Langues : |
Anglais (eng) |
| Descripteur : |
[Vedettes matières IGN] Traitement d'image
[Termes IGN] compression d'image
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] couleur (variable spectrale)
[Termes IGN] état de l'art
[Termes IGN] objet 3D
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] scène 3D
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
|
| Index. décimale : |
THESE Thèses et HDR |
| Résumé : |
(Auteur) With the rapid growth of multimedia content, 3D objects are becoming more and more popular. Most of the time, they are modeled as complex polygonal meshes or dense point clouds, providing immersive experiences in different industrial and consumer multimedia applications. The point cloud, which is easier to acquire than mesh and is widely applicable, has raised many interests in both the academic and commercial worlds.A point cloud is a set of points with different properties such as their geometrical locations and the associated attributes (e.g., color, material properties, etc.). The number of the points within a point cloud can range from a thousand, to constitute simple 3D objects, up to billions, to realistically represent complex 3D scenes. Such huge amounts of data bring great technological challenges in terms of transmission, processing, and storage of point clouds.In recent years, numerous research works focused their efforts on the compression of meshes, while less was addressed for point clouds. We have identified two main approaches in the literature: a purely geometric one based on octree decomposition, and a hybrid one based on both geometry and video coding. The first approach can provide accurate 3D geometry information but contains weak temporal consistency. The second one can efficiently remove the temporal redundancy yet a decrease of geometrical precision can be observed after the projection. Thus, the tradeoff between compression efficiency and accurate prediction needs to be optimized.We focused on exploring the temporal correlations between dynamic dense point clouds. We proposed different approaches to improve the compression performance of the MPEG (Moving Picture Experts Group) V-PCC (Video-based Point Cloud Compression) test model, which provides state-of-the-art compression on dynamic dense point clouds.First, an octree-based adaptive segmentation is proposed to cluster the points with different motion amplitudes into 3D cubes. Then, motion estimation is applied to these cubes using affine transformation. Gains in terms of rate-distortion (RD) performance have been observed in sequences with relatively low motion amplitudes. However, the cost of building an octree for the dense point cloud remains expensive while the resulting octree structures contain poor temporal consistency for the sequences with higher motion amplitudes.An anatomical structure is then proposed to model the motion of the point clouds representing humanoids more inherently. With the help of 2D pose estimation tools, the motion is estimated from 14 anatomical segments using affine transformation.Moreover, we propose a novel solution for color prediction and discuss the residual coding from prediction. It is shown that instead of encoding redundant texture information, it is more valuable to code the residuals, which leads to a better RD performance.Although our contributions have improved the performances of the V-PCC test models, the temporal compression of dynamic point clouds remains a highly challenging task. Due to the limitations of the current acquisition technology, the acquired point clouds can be noisy in both geometry and attribute domains, which makes it challenging to achieve accurate motion estimation. In future studies, the technologies used for 3D meshes may be exploited and adapted to provide temporal-consistent connectivity information between dynamic 3D point clouds. |
| Note de contenu : |
Chapter 1 - Introduction
1.1. Background and motivation
1.2. Outline of the thesis and contributions
Chapter 2 - 3D Point Cloud Compression: State of the art
2.1. The 3D PCC “Universe Map” for methods
2.2. 1D methods: geometry traversal
2.3. 2D methods: Projection and mapping onto 2D planar domains
2.4. 3D methods: Direct exploitation of 3D correlations
2.5. DL-based methods
2.6. 3D PCC: What is missing?
2.7. MPEG 3D PCC standards
Chapter 3 - Extended Study of MPEG V-PCC and G-PCC Approaches
3.1. V-PCC methodology
3.2. Experimental evaluation of V-PCC
3.3. G-PCC methodology
3.4. Experimental evaluation of G-PCC
3.5. Experiments on the V-PCC inter-coding mode
3.6. Conclusion
Chapter 4 - Octree-based RDO segmentation
4.1. Pipeline
4.2. RDO-based octree segmentation
4.3. Prediction modeS
4.4. Experimental results
4.5. Conclusion
Chapter 5 - Skeleton-based motion estimation and compensation
5.1. Introduction
5.2. 3D Skeleton Generation
5.3. Motion estimation and compression
5.4. Experimental results
5.5. Conclusion
Chapter 6 - Temporal prediction using anatomical segmentation
6.1. Introduction
6.2. A novel dynamic 3D point cloud dataset
6.3. Prediction structure
6.4. Improved anatomy segmentation
6.5. Experimental results
6.6. Conclusion
Chapter 7 - A novel color compression for point clouds using affine transformation
7.1. Introduction
7.2. The residuals from both geometry and color
7.3. The prediction structure
7.4. Compression of the color residuals
7.5. Experimental results
7.6. Conclusion
Chapter 8 - Conclusion and future work
8.1. Conclusion
8.2. Future work
|
| Numéro de notice : |
26821 |
| Affiliation des auteurs : |
non IGN |
| Thématique : |
IMAGERIE/INFORMATIQUE |
| Nature : |
Thèse française |
| Note de thèse : |
Thèse de Doctorat : informatique : Paris : 2021 |
| Organisme de stage : |
Telecom SudParis |
| nature-HAL : |
Thèse |
| DOI : |
sans |
| Date de publication en ligne : |
13/04/2022 |
| En ligne : |
https://tel.hal.science/tel-03524521 |
| Format de la ressource électronique : |
URL |
| Permalink : |
https://documentation.ensg.eu/index.php?lvl=notice_display&id=100476 |
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3D point cloud compression (2021)
