Lam Mai-Thanh
ABSTRACT
PointNet++ has gained significant acknowledgement for point cloud data processing capabilities. Over time, various network improvements have been developed to enhance its global learning efficiency, thus boosting the correct segmentation rate. However, these improvements have often resulted in a significant increase in complexity, i.e., the model size and the processing speed. Meanwhile, improvements that focus on complexity reduction while preserving accuracy have been relatively scarce, particularly compared to some simpler models like SqueezeSegV2. To overcome this challenge, we embark on the development of a compact version of the PointNet++ model, namely PointGANet, tailored specifically for three-dimensional point cloud semantic segmentation. In PointGANet, we introduce a grouped attention mechanism in an encoder with grouped convolution incorporated with element-wise multiplication to enrich feature extraction capability and emphasise relevant features. In a decoder, we replace unit pointnet modules with mini pointnet modules to save a massive number of trainable parameters. Through rigorous experimentation, we successfully fine-tune the network to obtain a significant reduction in model size while maintaining accuracy, hence resulting in a substantial enhancement in overall performance. Remarkably, relying on the intensive evaluation using the DALES dataset, PointGANet is more lightweight than the original PointNet++ by approximately five times with some noteworthy improvements in mean accuracy by and mean IoU . These innovations open up exciting possibilities for developing point cloud segmentation applications on IoT and resource-constrained devices.
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Index Terms
- PointGANet: A Lightweight 3D Point Cloud Learning Architecture for Semantic Segmentation
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