3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy

TL;DR

This paper introduces 3DARFV, a probabilistic decision tree-based method for 3D image segmentation in planetary exploration, achieving higher efficiency and comparable accuracy to deep learning methods without relying on high computational resources.

Contribution

The paper presents 3DARFV, a novel 3D adapted random forest algorithm that surpasses deep learning in efficiency while maintaining high accuracy for semantic segmentation.

Findings

3DARFV outperforms deep learning in efficiency for 3D segmentation.

The method maintains high accuracy comparable to deep learning approaches.

Reduced computational requirements enable real-time analysis in resource-limited environments.

Abstract

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.