3D自适应随机森林视觉（3DARFV）用于解开异质结构，以最高精度超越深度学习语义分割效率
3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy
摘要：
行星探测在很大程度上依赖于3D图像数据来表征岩石和环境的静态和动态特性。分析3D图像需要大量的计算，导致效率受到冗长的处理时间以及大量的能源消耗的影响。高性能计算（HPC）以能耗为代价提供了明显的效率。然而，对于远程探索，传输监视和机器人传感需要更快的数据分析，以最终的准确性做出实时决策。在这样的环境中，对HPC和能源的访问是有限的。因此，我们认识到，减少计算的数量，以最佳和保持所需的精度导致更高的效率。本文展示了概率决策树算法3D自适应随机森林视觉（3DARFV）的语义分割能力，在最高精度上超过了深度学习算法的效率。
英文摘要：
Planetary exploration depends heavily on 3D image data to characterize the static and dynamic properties of the rock and environment. Analyzing 3D images requires many computations, causing efficiency to suffer lengthy processing time alongside large energy consumption. High-Performance Computing (HPC) provides apparent efficiency at the expense of energy consumption. However, for remote explorations, the conveyed surveillance and the robotized sensing need faster data analysis with ultimate accuracy to make real-time decisions. In such environments, access to HPC and energy is limited. Therefore, we realize that reducing the number of computations to optimal and maintaining the desired accuracy leads to higher efficiency. This paper demonstrates the semantic segmentation capability of a probabilistic decision tree algorithm, 3D Adapted Random Forest Vision (3DARFV), exceeding deep learning algorithm efficiency at the utmost accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
