Proceedings ArticleDOI
Geometric distortion metrics for point cloud compression
Dong Tian,Hideaki Ochimizu,Chen Feng,Robert A. Cohen,Anthony Vetro +4 more
- pp 3460-3464
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TLDR
The intrinsic resolution of the point clouds is proposed as a normalizer to convert the mean square errors to PSNR numbers and this method could better track the perceived quality than the point-to-point approach while requires limited computations.Abstract:
It is challenging to measure the geometry distortion of point cloud introduced by point cloud compression. Conventionally, the errors between point clouds are measured in terms of point-to-point or point-to-surface distances, that either ignores the surface structures or heavily tends to rely on specific surface reconstructions. To overcome these drawbacks, we propose using point-to-plane distances as a measure of geometric distortions on point cloud compression. The intrinsic resolution of the point clouds is proposed as a normalizer to convert the mean square errors to PSNR numbers. In addition, the perceived local planes are investigated at different scales of the point cloud. Finally, the proposed metric is independent of the size of the point cloud and rather reveals the geometric fidelity of the point cloud. From experiments, we demonstrate that our method could better track the perceived quality than the point-to-point approach while requires limited computations.read more
Citations
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Journal ArticleDOI
Emerging MPEG Standards for Point Cloud Compression
Sebastian Schwarz,Marius Preda,Vittorio Baroncini,Madhukar Budagavi,Pablo Cesar,Philip A. Chou,Robert A. Cohen,Maja Krivokuca,Sebastien Lasserre,Zhu Li,Joan Llach,Mammou Khaled,Rufael Mekuria,Ohji Nakagami,Ernestasia Siahaan,Ali Tabatabai,Alexis Michael Tourapis,Vladyslav Zakharchenko +17 more
TL;DR: The main developments and technical aspects of this ongoing standardization effort for compactly representing 3D point clouds, which are the 3D equivalent of the very well-known 2D pixels are introduced.
Proceedings ArticleDOI
Point Cloud Quality Assessment Metric Based on Angular Similarity
TL;DR: Correlation with subjective quality assessment scores carried out by human subjects shows the proposed metric to be superior to the state of the art in terms of predicting the visual quality of point clouds under realistic types of distortions, such as octree-based compression.
Journal ArticleDOI
Lossy Point Cloud Geometry Compression via End-to-End Learning
TL;DR: A novel end-to-end Learned Point Cloud Geometry Compression framework, to efficiently compress the point cloud geometry using deep neural networks (DNN) based variational autoencoders (VAE), which exceeds the geometry-based point cloud compression (G-PCC) algorithm standardized by well-known Moving Picture Experts Group (MPEG).
Proceedings ArticleDOI
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
TL;DR: In this article, the authors proposed a data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. And they cast the decoding process as a binary classification of the point cloud occupancy map.
Proceedings ArticleDOI
Towards a Point Cloud Structural Similarity Metric
TL;DR: A family of statistical dispersion measurements for the prediction of perceptual degradations is proposed and assessed, and best-performing attributes and features are revealed, under different neighborhood sizes.
References
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Proceedings ArticleDOI
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