Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edge

2026-08-07

AI Summary

This work addresses key challenges in deploying 3D deep learning on edge devices, including the unstructured nature of point clouds, high computational costs of conventional preprocessing, and performance degradation caused by domain gaps between synthetic CAD models and real-world LiDAR data. To bridge this domain discrepancy, the authors propose a sensor-aware physically simulated LiDAR data generation method. Furthermore, they introduce a deterministic Critical Point Layer (CPL) that enables efficient point cloud compression without requiring distance-based sorting. Integrated with an ARM Cortex-A76-optimized lightweight classification network, the system compresses input point clouds from 1,024 to 40–60 points and achieves real-time inference at approximately 50 FPS on a Raspberry Pi 5, attaining a classification accuracy of 88.36%.

Abstract

Deploying three-dimensional deep learning frameworks to low-power embedded processors is bottlenecked by the unstructured nature of spatial data and the resource-intensive distance sorting algorithms often used before neural network inference. To address this gap, this paper presents a hardware-constrained workflow optimized for native execution on the Raspberry Pi 5. To account for the reality gap between noiseless, clean computer-aided design (CAD) datasets and real-world sensor data, we use physics-based simulation to construct a synthetic LiDAR dataset. Cross-dataset evaluations demonstrate a substantial drop in classification accuracy when networks trained on clean CAD data are evaluated on synthetic LiDAR sensor data, highlighting the critical need for sensor-aware training. To address the latency bottleneck of traditional geometric preprocessing on edge CPUs, we integrate an isolated, feature-driven Critical Points Layer (CPL) as a frontend filter. Our results show that the pretrained CPL deterministically compresses raw 1024-point clouds to a subset of 40 to 60 unique coordinates. When profiled on the ARM Cortex-A76 processor, the complete pipeline achieves an inference throughput of approximately 50 FPS while maintaining an instance classification accuracy of 88.36%, demonstrating the viability of deterministic real-time 3D perception at the edge.

Problem

• LiDAR simulation
• point cloud classification
• edge computing
• reality gap
• real-time inference

Innovation

• synthetic LiDAR
• deterministic downsampling
• edge computing
• Critical Points Layer
• point cloud classification