PointEMRay: A Novel Efficient SBR Framework on Point Based Geometry

TL;DR

PointEMRay introduces a novel, efficient SBR framework optimized for point cloud geometries, enabling accurate and real-time electromagnetic field simulations by leveraging deep learning and GFB-assisted methods.

Contribution

This work is the first to develop an SBR framework specifically designed for point-based models, integrating deep learning and GFB techniques for improved accuracy and efficiency.

Findings

Demonstrates superior accuracy over existing methods.

Supports real-time electromagnetic simulation.

Effectively handles complex point cloud geometries.

Abstract

The rapid computation of electromagnetic (EM) fields across various scenarios has long been a challenge, primarily due to the need for precise geometric models. The emergence of point cloud data offers a potential solution to this issue. However, the lack of electromagnetic simulation algorithms optimized for point-based models remains a significant limitation. In this study, we propose PointEMRay, an innovative shooting and bouncing ray (SBR) framework designed explicitly for point-based geometries. To enable SBR on point clouds, we address two critical challenges: point-ray intersection (PRI) and multiple bounce computation (MBC). For PRI, we propose a screen-based method leveraging deep learning. Initially, we obtain coarse depth maps through ray tube tracing, which are then transformed by a neural network into dense depth maps, normal maps, and intersection masks, collectively referred to as geometric frame buffers (GFBs). For MBC, inspired by simultaneous localization and mapping (SLAM) techniques, we introduce a GFB-assisted approach. This involves aggregating GFBs from various observation angles and integrating them to recover the complete geometry. Subsequently, a ray tracing algorithm is applied to these GFBs to compute the scattering electromagnetic field. Numerical experiments demonstrate the superior performance of PointEMRay in terms of both accuracy and efficiency, including support for real-time simulation. To the best of our knowledge, this study represents the first attempt to develop an SBR framework specifically tailored for point-based models.