Point Cloud Neighborhood Estimation Method Using Deep Neuro-Evolution

Ahmed Abouelazm, Igor Vozniak, Nils Lipp, Pavel Astreika, Christian Müller

Abstract: Due to the recent advancements in computing hardware, deep learning, and 3D sensors, point clouds have become an essential 3D data structure, and their processing and analysis have received considerable attention. Given the unstructured and irregular nature of point clouds, encoding local geometries is a significant barrier in point cloud analysis. The aforementioned challenge is known as neighborhood estimation, and it is commonly addressed by fitting a plane to points within a local neighborhood defined by estimated parameters. The estimated neighborhood parameters for each point should adapt to the point cloud’s irregularities and different local geometries’ sizes and shapes. Different objective functions have been derived in the literature for optimal parameters selection with no efficient approach for these objective functions’ optimization as of now. In this work, we propose a novel neighborhood estimation pipeline for such optimization which is objective function and neighborh