TY - GEN
T1 - Pushing Point Cloud Compression to the Edge
AU - Ying, Ziyu
AU - Zhao, Shulin
AU - Bhuyan, Sandeepa
AU - Mishra, Cyan Subhra
AU - Kandemir, Mahmut T.
AU - Das, Chita R.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 × performance improvement and 96.6% energy savings at a cost of 1.01 × 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 × speedup compared to a state-of-the-art scheme, with minimal impact on quality and compression ratio.
AB - 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 × performance improvement and 96.6% energy savings at a cost of 1.01 × 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 × speedup compared to a state-of-the-art scheme, with minimal impact on quality and compression ratio.
UR - http://www.scopus.com/inward/record.url?scp=85141689456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141689456&partnerID=8YFLogxK
U2 - 10.1109/MICRO56248.2022.00031
DO - 10.1109/MICRO56248.2022.00031
M3 - Conference contribution
AN - SCOPUS:85141689456
T3 - Proceedings of the Annual International Symposium on Microarchitecture, MICRO
SP - 282
EP - 299
BT - Proceedings - 2022 55th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2022
PB - IEEE Computer Society
T2 - 55th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2022
Y2 - 1 October 2022 through 5 October 2022
ER -