Edge AI Achieves Real-time 3D Perception with Synthetic LiDAR Data

Deploying 3D deep learning models on resource-constrained edge devices, like the Raspberry Pi 5, faces significant challenges due to the complex nature of spatial data and the computational demands of traditional preprocessing. Researchers have developed a new workflow specifically optimized for these hardware limitations. This workflow addresses the "reality gap" between clean computer-aided design (CAD) datasets and real-world sensor data by generating synthetic LiDAR data through physics-based simulations, which proved crucial for training robust models. A key innovation is the integration of a Critical Points Layer (CPL) as a frontend filter. This CPL deterministically compresses raw 1024-point clouds into a much smaller subset of 40 to 60 unique coordinates, significantly reducing the latency bottleneck typically associated with geometric preprocessing on edge CPUs. When tested on an ARM Cortex-A76 processor, the complete system achieved an impressive inference throughput of approximately 50 frames per second, while maintaining an instance classification accuracy of 88.36%. This demonstrates a practical solution for real-time 3D perception on edge devices.