Lite-GrSeg: Lightweight Architecture for 3D Point Cloud Road-Scene Semantic Segmentation

Recently, point cloud semantic segmentation has played an important role in real-world applications such as autonomous driving and robotics. In this context, while recognized as an efficient semantic segmentation model with a good balance between performance and complexity, SqueezeSegv2 is still too heavy for resource-constrained devices. In this paper, we propose Lite-GrSeg, a compact and effective semantic segmentation model inspired by SqueezeSegv2. Lite-GrSeg adopts a cutting-edge design architecture that leverages SqueezeSegV2 with group convolution and spatial separable convolution to reduce the model’s complexity. Additionally, Lite-GrSeg introduces a novel structure called the Spatial Context Aggregation Module (Spatial-CAM) to enhance the model’s discriminability. Through the simulations benchmarked on the PandaSet dataset, Lite-GrSeg significantly reduces computational complexity and model size while presenting competitive segmentation accuracy compared to SqueezeSegV2, thus making it a compelling choice for lightweight applications and opening up exciting possibilities for development on resource-constrained IoT devices.