Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing

TL;DR

This paper introduces an evolutionary optimization method to fit primitive 3D shapes, specifically cylinders, to noisy and incomplete point clouds, enabling better modeling of complex scenes from imperfect data.

Contribution

The paper presents a novel evolutionary approach with adaptive mechanisms for fitting primitive shapes to partial, noisy point clouds, including new mutation operators for improved search.

Findings

Effective in modeling complex scenes with noisy data

Demonstrated robustness on synthetic and real-world datasets

Outperforms existing methods in shape approximation accuracy

Abstract

Point clouds obtained from photogrammetry are noisy and incomplete models of reality. We propose an evolutionary optimization methodology that is able to approximate the underlying object geometry on such point clouds. This approach assumes a priori knowledge on the 3D structure modeled and enables the identification of a collection of primitive shapes approximating the scene. Built-in mechanisms that enforce high shape diversity and adaptive population size make this method suitable to modeling both simple and complex scenes. We focus here on the case of cylinder approximations and we describe, test, and compare a set of mutation operators designed for optimal exploration of their search space. We assess the robustness and limitations of this algorithm through a series of synthetic examples, and we finally demonstrate its general applicability on two real-life cases in vegetation and industrial settings.