Pushing Point Cloud Compression to the Edge
Pushing Point Cloud Compression to the Edge
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DOI：
10.1109/micro56248.2022.00031
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ziyu Ying;Shulin Zhao;Sandeepa Bhuyan;Cyan Subhra Mishra;M. Kandemir;C. Das
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文献类型：
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作者：
Ziyu Ying;Shulin Zhao;Sandeepa Bhuyan;Cyan Subhra Mishra;M. Kandemir;C. Das
随着点云（PC）在处理许多应用程序中的3D渲染的数百万个数据点方面的流行，有效的数据压缩将成为一个关键问题。这是因为压缩是最大程度地减少现有PC管道的延迟和能耗的主要瓶颈。随着PC处理被推到具有有限的计算和功率预算的边缘设备，数据压缩变得更加关键。在本文中，我们提出和评估两种互补方案，即框内压缩和框架间压缩，以加快PC压缩，而不会失去质量或压缩效率太大。与使用顺序算法的现有技术不同，我们的第一个设计，框内压缩，利用并行性来提高几何和属性压缩的性能。拟议的并行性带来了$ 43.7 \ times $ $绩效的改进和96.6％的能源节省，成本为$ 1.01 \ times $ $较大的压缩数据大小。为了进一步提高压缩效率，我们的第二个方案，框架间的压缩考虑了视频帧之间的时间相似性，并重用当前帧上一个帧中的属性数据。我们在NVIDIA JETSON AGX XAVIER EDGE GPU板上实施设计。六个视频的实验结果表明，与最先进的方案相比，合并的压缩方案提供了$ 34.0 \ times $速度，对质量和压缩比的影响最小。
As Point Clouds (PCs) gain popularity in processing millions of data points for 3D rendering in many applications, efficient data compression becomes a critical issue. This is because compression is the primary bottleneck in minimizing the latency and energy consumption of existing PC pipelines. Data compression becomes even more critical as PC processing is pushed to edge devices with limited compute and power budgets. In this paper, we propose and evaluate two complementary schemes, intra-frame compression and inter-frame compression, to speed up the PC compression, without losing much quality or compression efficiency. Unlike existing techniques that use sequential algorithms, our first design, intra-frame compression, exploits parallelism for boosting the performance of both geometry and attribute compression. The proposed parallelism brings around $43.7 \times$ performance improvement and 96.6% energy savings at a cost of $1.01 \times$ larger compressed data size. To further improve the compression efficiency, our second scheme, inter-frame compression, considers the temporal similarity among the video frames and reuses the attribute data from the previous frame for the current frame. We implement our designs on an NVIDIA Jetson AGX Xavier edge GPU board. Experimental results with six videos show that the combined compression schemes provide $34.0 \times$ speedup compared to a state-of-the-art scheme, with minimal impact on quality and compression ratio.