The spatial resolution of a depth-acquisition device, such as a Light Detection and Ranging (LiDAR) sensor, is limited because of the slow acquisition. To accurately reconstruct a depth image from limited spatial resolution, a two-stage sampling process has been widely used. However, two-stage sampling uses an irregular sampling pattern for the sampling operation, which requires complex computation for reconstruction and additional memory space for storage. A mathematical formulation of a LiDAR system demonstrates that two-stage sampling does not satisfy its timing constraint for practical use. To overcome the drawbacks of two-stage sampling, this paper proposes a new sampling method that reduces the computational complexity and memory requirements by generating the optimal representatives of a sampling pattern in down-sample data. A sampling pattern can be derived from a $k$ -NN expanding operation from the down-sampled representatives. The proposed algorithm is designed to preserve the object boundary by restricting the expansion-operation only to the object boundary or complex texture. In addition, the proposed algorithm runs in linear-time complexity and reduces the memory requirements using a down-sampling ratio. The experimental results demonstrate that the proposed sampling outperforms grid sampling by at most 7.92 dB. Consequently, the proposed sampling achieves reconstructed quality similar to that of optimal sampling, while substantially reducing the computation time and memory requirements.

中文翻译：

---

一种用于 LiDAR 系统深度数据采集的具有 K-NN 扩展算子的高效采样算法

由于采集速度慢，深度采集设备（例如光探测和测距 (LiDAR) 传感器）的空间分辨率受到限制。为了从有限的空间分辨率准确重建深度图像，两阶段采样过程已被广泛使用。然而，两阶段采样使用不规则采样模式进行采样操作，这需要复杂的重构计算和额外的存储空间来存储。LiDAR 系统的数学公式表明，两级采样不满足其实际使用的时序约束。为了克服两阶段采样的缺点，本文提出了一种新的采样方法，通过在下采样数据中生成采样模式的最佳代表来降低计算复杂度和内存要求。采样模式可以从下采样代表的 $k$ -NN 扩展操作中导出。所提出的算法旨在通过将扩展操作仅限于对象边界或复杂纹理来保留对象边界。此外，所提出的算法以线性时间复杂度运行，并使用下采样率降低了内存需求。实验结果表明，所提出的采样最多优于网格采样 7.92 dB。因此，所提出的采样实现了类似于最佳采样的重建质量，同时大大减少了计算时间和内存要求。所提出的算法旨在通过将扩展操作仅限于对象边界或复杂纹理来保留对象边界。此外，所提出的算法以线性时间复杂度运行，并使用下采样率降低了内存需求。实验结果表明，所提出的采样最多优于网格采样 7.92 dB。因此，所提出的采样实现了类似于最佳采样的重建质量，同时大大减少了计算时间和内存要求。所提出的算法旨在通过将扩展操作仅限于对象边界或复杂纹理来保留对象边界。此外，所提出的算法以线性时间复杂度运行，并使用下采样率降低了内存需求。实验结果表明，所提出的采样最多优于网格采样 7.92 dB。因此，所提出的采样实现了类似于最佳采样的重建质量，同时大大减少了计算时间和内存要求。实验结果表明，所提出的采样最多优于网格采样 7.92 dB。因此，所提出的采样实现了类似于最佳采样的重建质量，同时大大减少了计算时间和内存要求。实验结果表明，所提出的采样最多优于网格采样 7.92 dB。因此，所提出的采样实现了类似于最佳采样的重建质量，同时大大减少了计算时间和内存要求。