New technology has emerged involving Brain–Computer Interfaces and Neuroengineering such that new means of sensory enhancement that integrate sensing in the environment and direct neural stimulation have evolved. This paper introduces a novel framework for transforming LiDAR-based 3D environmental data into neural-compatible signals intended for visual cortex stimulation. Leveraging a two-stage approach, the model first projects point cloud data into structured 2D images and then uses a Convolutional Neural Network to simulate biologically plausible cortical activation patterns. The generated oscillatory signals closely resemble real visual cortex responses, supporting the viability of this pipeline for future brain–computer interfaces and sensory augmentation. Initial simulation results suggest potential for non-invasive visual restoration. Future work will focus on enhancing signal encoding, integrating object recognition, and deploying adaptive processing for real-world conditions.