论文标题
肌肉：使用深熵模型对LIDAR进行多扫描压缩
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
论文作者
论文摘要
我们提出了一种新型的压缩算法，用于减少LIDAR传感器数据流的存储。我们的模型利用了跨多个LiDAR扫描的时空关系，以降低几何和强度值的比特率。为了实现这一目标，我们提出了一个新颖的条件熵模型，该模型通过考虑粗级几何形状和以前的扫描的几何和强度信息来对OCTREE符号的概率进行建模。然后，我们使用学习的概率将完整的数据流编码为紧凑的数据流。我们的实验表明，我们的方法显着降低了先前的最新痛压缩方法的关节几何形状和强度比特率，分别在Urbancity和Semantickitti数据集上降低了7-17％和15-35％。
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values. Towards this goal, we propose a novel conditional entropy model that models the probabilities of the octree symbols by considering both coarse level geometry and previous sweeps' geometric and intensity information. We then use the learned probability to encode the full data stream into a compact one. Our experiments demonstrate that our method significantly reduces the joint geometry and intensity bitrate over prior state-of-the-art LiDAR compression methods, with a reduction of 7-17% and 15-35% on the UrbanCity and SemanticKITTI datasets respectively.
