# 3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a Technical Vision System

## TL;DR

This paper introduces a method to improve 3D scanning using a low-resolution system by reducing noise and reconstructing detailed models.

## Contribution

A novel data processing and entropy reduction technique for 3D scanning using a low-resolution technical vision system.

## Key findings

- Optimal scanning windows and statistical filtering reduce noise and entropy in 3D scans.
- 3D models can be reconstructed with sufficient detail from low-resolution and noisy data.
- Registration methods align TVS scans with ground truth models to verify reconstruction accuracy.

## Abstract

This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy.