增强效率：通过混合视觉减少3D语义分割的内存占用并加速推理
Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision
AI总结：
本文提出一种融合2D与3D视觉技术的3D语义分割方法，通过对RGB图像进行2D分割并挤压扩展至3D点云，在KITTI-360数据集上实现1.347倍加速和约43%内存减少，同时保持接近基线的精度。
摘要：
语义分割已成为计算机视觉研究的关键领域，对场景理解及提升各领域的人机交互具有深远影响。尽管2D语义分割在轻量级、高精度模型方面取得了显著进展，但向3D语义分割的过渡面临独特挑战。我们的研究旨在实现3D语义分割模型的效率和轻量化设计，使其达到与2D模型相似的水平。这种设计对关注内存和延迟的3D语义分割应用具有重要影响。本文提出了一种新颖的3D语义分割方法，其特点在于融合了2D和3D计算机视觉技术的混合方法，从而实现精简高效的流程。我们对与3D点云关联的RGB图像进行2D语义分割，并使用针对特定类标签的挤压技术将结果扩展到3D，以减少点云子空间。我们以DeepViewAgg模型在完整点云上的表现作为基线进行严格评估，测量交并比（IoU）精度、推理时间延迟和内存消耗。该模型是KITTI-360数据集上当前最先进的3D语义分割模型。我们实现了更高的精度，在15个类别中有6个超越了基线，同时其余类别标签的精度保持在低于基线1%的微小偏差范围内。与基线相比，我们的分割方法实现了1.347倍加速和约43%的内存使用减少。
英文摘要：
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.
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