  Semantic segmentation has emerged as a pivotal area of study in computer
vision, offering profound implications for scene understanding and elevating
human-machine interactions across various domains. While 2D semantic
segmentation has witnessed significant strides in the form of lightweight,
high-precision models, transitioning to 3D semantic segmentation poses distinct
challenges. Our research focuses on achieving efficiency and lightweight design
for 3D semantic segmentation models, similar to those achieved for 2D models.
Such a design impacts applications of 3D semantic segmentation where memory and
latency are of concern. This paper introduces a novel approach to 3D semantic
segmentation, distinguished by incorporating a hybrid blend of 2D and 3D
computer vision techniques, enabling a streamlined, efficient process.
  We conduct 2D semantic segmentation on RGB images linked to 3D point clouds
and extend the results to 3D using an extrusion technique for specific class
labels, reducing the point cloud subspace. We perform rigorous evaluations with
the DeepViewAgg model on the complete point cloud as our baseline by measuring
the Intersection over Union (IoU) accuracy, inference time latency, and memory
consumption. This model serves as the current state-of-the-art 3D semantic
segmentation model on the KITTI-360 dataset. We can achieve heightened accuracy
outcomes, surpassing the baseline for 6 out of the 15 classes while maintaining
a marginal 1% deviation below the baseline for the remaining class labels. Our
segmentation approach demonstrates a 1.347x speedup and about a 43% reduced
memory usage compared to the baseline.
