NeuroSpatial3D: Event-Driven Neuromorphic Vision for Real-Time 3D Scene Reconstruction and Spatial Understanding

Abstract

The convergence of neuromorphic computing and 3D scene reconstruction represents a paradigm shift toward ultra-low-power, real-time spatial understanding systems. This paper introduces NeuroSpatial3D, a novel framework that harnesses event-driven neuromorphic vision sensors and spiking neural networks to achieve real-time 3D scene reconstruction with unprecedented energy efficiency. Our approach addresses three fundamental challenges in conventional 3D reconstruction: temporal resolution limitations through Asynchronous Event Processing (AEP), computational efficiency via Bio-Inspired Depth Estimation (BIDE), and dynamic scene handling using Spike-Based Temporal Fusion (SBTF). NeuroSpatial3D achieves remarkable performance with 94.8% reconstruction accuracy on dynamic scenes while consuming only 2.3mW power, rep- resenting a 1000x energy reduction compared to traditional RGB-based methods. Comprehensive evaluation across diverse indoor and outdoor environments demonstrates consistent superiority: 15.7% improvement in reconstruction quality, 250x faster processing speed, and 45ms end-to-end latency enabling real-time deployment. The framework enables break- through applications in autonomous robotics, augmented reality, and IoT systems where power constraints and real-time requirements are critical. Our work establishes neuromorphic vision as a transformative technology for next-generation spatial computing applications.