Papers and Publications
2024
Gomez-Donoso, Francisco; Escalona, Felix; Dargere, Florian; Cazorla, Miguel
VFLD: Voxelized Fractal Local Descriptor Journal Article
In: Applied Sciences, vol. 14, no. 20, 2024, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags: 3D processing, AI, computer vision, fractal
@article{app14209414,
title = {VFLD: Voxelized Fractal Local Descriptor},
author = {Francisco Gomez-Donoso and Felix Escalona and Florian Dargere and Miguel Cazorla},
url = {https://www.mdpi.com/2076-3417/14/20/9414},
issn = {2076-3417},
year = {2024},
date = {2024-01-01},
journal = {Applied Sciences},
volume = {14},
number = {20},
abstract = {A variety of methods for 3D object recognition and registration based on a deep learning pipeline have recently emerged. Nonetheless, these methods require large amounts of data that are not easy to obtain, sometimes rendering them virtually useless in real-life scenarios due to a lack of generalization capabilities. To counter this, we propose a novel local descriptor that takes advantage of the fractal dimension. For each 3D point, we create a descriptor by computing the fractal dimension of the neighbors at different radii. Our redmethod has many benefits, such as being agnostic to the sensor of choice and noise, up to a level, and having few parameters to tinker with. Furthermore, it requires no training and does not rely on semantic information. We test our descriptor using well-known datasets and it largely outperforms Fast Point Feature Histogram, which is the state-of-the-art descriptor for 3D data. We also apply our descriptor to a registration pipeline and achieve accurate three-dimensional representations of the scenes, which are captured with a commercial sensor.},
keywords = {3D processing, AI, computer vision, fractal},
pubstate = {published},
tppubtype = {article}
}
A variety of methods for 3D object recognition and registration based on a deep learning pipeline have recently emerged. Nonetheless, these methods require large amounts of data that are not easy to obtain, sometimes rendering them virtually useless in real-life scenarios due to a lack of generalization capabilities. To counter this, we propose a novel local descriptor that takes advantage of the fractal dimension. For each 3D point, we create a descriptor by computing the fractal dimension of the neighbors at different radii. Our redmethod has many benefits, such as being agnostic to the sensor of choice and noise, up to a level, and having few parameters to tinker with. Furthermore, it requires no training and does not rely on semantic information. We test our descriptor using well-known datasets and it largely outperforms Fast Point Feature Histogram, which is the state-of-the-art descriptor for 3D data. We also apply our descriptor to a registration pipeline and achieve accurate three-dimensional representations of the scenes, which are captured with a commercial sensor.