@inproceedings{e9178897e47c4320a9bcc383912fd0a5,
title = "A Dynamic 3D Point Cloud Dataset for Immersive Applications",
abstract = "Motion estimation in a 3D point cloud sequence is a fundamental operation with many applications, including compression, error concealment, and temporal upscaling. While there have been multiple research contributions toward estimating the motion vector of points between frames, there is a lack of a dynamic 3D point cloud dataset with motion ground truth to benchmark against. In this paper, we present an open dynamic 3D point cloud dataset to fill this gap. Our dataset consists of synthetically generated objects with pre-determined motion patterns, allowing us to generate the motion vectors for the points. Our dataset contains nine objects in three categories (shape, avatar, and textile) with different animation patterns. We also provide semantic segmentation of each avatar object in the dataset. Our dataset can be used by researchers who need temporal information across frames. As an example, we present an evaluation of two motion estimation methods using our dataset.",
keywords = "dataset, error concealment, immersive applications, interpolation, point cloud, point matching, register",
author = "Sun, {Yuan Chun} and Huang, {I. Chun} and Yuang Shi and Ooi, {Wei Tsang} and Huang, {Chun Ying} and Hsu, {Cheng Hsin}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 14th ACM Multimedia Systems Conference, MMSys 2023 ; Conference date: 07-06-2023 Through 10-06-2023",
year = "2023",
month = jun,
day = "7",
doi = "10.1145/3587819.3592546",
language = "English",
series = "MMSys 2023 - Proceedings of the 14th ACM Multimedia Systems Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "376--383",
booktitle = "MMSys 2023 - Proceedings of the 14th ACM Multimedia Systems Conference",
}