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

arXiv: https://arxiv.org/abs/2203.12469 · HTML: https://mlsanity.com/paper/2203.12469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Automated Machine Learning: From Principles to Practices](https://mlsanity.com/paper/1810.13306/) (2018)
- [Amazon SageMaker Autopilot: a white box AutoML solution at scale](https://mlsanity.com/paper/2012.08483/) (2020)
- [Efficient Machine Learning for Big Data: A Review](https://mlsanity.com/paper/1503.05296/) (2015)
- [Efficiently measuring a quantum device using machine learning](https://mlsanity.com/paper/1810.10042/) (2018)
- [Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination](https://mlsanity.com/paper/2111.04612/) (2021)
- [Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions](https://mlsanity.com/paper/2111.13460/) (2021)
- [Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation](https://mlsanity.com/paper/2112.15068/) (2021)

## Abstract (arXiv)

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.