论文标题
3D适用于随机森林视觉（3DARFV），用于弄清异质性的，超过深度学习语义分割效率，以最高的准确性
3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy
论文作者
论文摘要
行星探索在很大程度上取决于3D图像数据，以表征岩石和环境的静态和动态特性。分析3D图像需要许多计算，从而导致效率与大量能耗一起遭受冗长的处理时间。高性能计算（HPC）提供明显的效率，而牺牲了能源消耗。但是，对于远程探索，传达的监视和机器人感知需要更快的数据分析，以最终的准确性才能做出实时决策。在这种环境中，获得HPC和能源的访问受到限制。因此，我们意识到，将计算数量减少到最佳和维持所需的准确性会导致更高的效率。本文展示了概率决策树算法的语义分割能力，3D适应了随机森林视觉（3DARFV），以最高的精度超过了深度学习算法效率。
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.
