论文标题
Octsqueeze：OCTREE结构熵模型，用于激光雷达压缩
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
论文作者
论文摘要
我们提出了一种新型的深层压缩算法，以减少LiDAR点云的记忆足迹。我们的方法利用了点之间的稀疏性和结构冗余，以降低比特率。为了实现这一目标，我们首先将LiDar点编码到OCTREE，这是一种适合稀疏点云的数据有效结构。然后，我们设计了一个树结构的条件熵模型，该模型对OCTREE符号的概率进行建模，以将OCTREE编码为紧凑的Bitstream。我们在两个大规模数据集上验证了方法的有效性。结果表明，与先前的最新面积相比，我们的方法以相同的重建质量将比特率降低了10-20％。重要的是，我们还表明，对于相同的比特率，我们的方法在使用压缩表示的下游3D分割和检测任务时，在执行下游3D分割和检测任务时的其他压缩算法优于其他压缩算法。我们的算法可用于减少诸如自动驾驶汽车之类的应用LIDAR点的机载和板储存，其中一辆车每天捕获840亿点
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the bitrate. Towards this goal, we first encode the LiDAR points into an octree, a data-efficient structure suitable for sparse point clouds. We then design a tree-structured conditional entropy model that models the probabilities of the octree symbols to encode the octree into a compact bitstream. We validate the effectiveness of our method over two large-scale datasets. The results demonstrate that our approach reduces the bitrate by 10-20% at the same reconstruction quality, compared to the previous state-of-the-art. Importantly, we also show that for the same bitrate, our approach outperforms other compression algorithms when performing downstream 3D segmentation and detection tasks using compressed representations. Our algorithm can be used to reduce the onboard and offboard storage of LiDAR points for applications such as self-driving cars, where a single vehicle captures 84 billion points per day
