@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}

}