基于掩码自编码器自监督学习的注意力引导多尺度局部重建方法用于点云
Attention-Guided Multi-Scale Local Reconstruction for Point Clouds via Masked Autoencoder Self-Supervised Learning
AI总结：
本文提出PointAMaLR自监督学习框架，通过注意力引导的多尺度局部重建和局部注意力模块增强点云特征表示，在多个基准和真实数据集上取得优异性能。
摘要：
自监督学习已成为点云处理中一个突出的研究方向。现有模型主要集中于较高编码器层的重建任务，往往忽视了低层局部特征的有效利用，这些特征通常仅用于激活计算，而并未直接贡献于重建任务。为克服这一局限，我们提出了PointAMaLR，一种新颖的自监督学习框架，通过注意力引导的多尺度局部重建来增强特征表示和处理精度。PointAMaLR在多个局部区域上实施分层重建，较低层专注于细粒度特征恢复，而较高层处理粗粒度特征重建，从而实现复杂的块间交互。此外，为增强特征表示能力，我们在嵌入层中引入局部注意力（Local Attention, LA）模块，以提升语义特征理解。在基准数据集ModelNet和ShapeNet上的综合实验表明，PointAMaLR在分类和重建任务中均具有优越的准确性和质量。此外，在真实世界数据集ScanObjectNN和三维大场景分割数据集S3DIS上评估时，我们的模型取得了极具竞争力的性能指标。这些结果不仅验证了PointAMaLR在多尺度语义理解方面的有效性，也凸显了其在真实世界场景中的实际适用性。
英文摘要：
Self-supervised learning has emerged as a prominent research direction in point cloud processing. While existing models predominantly concentrate on reconstruction tasks at higher encoder layers, they often neglect the effective utilization of low-level local features, which are typically employed solely for activation computations rather than directly contributing to reconstruction tasks. To overcome this limitation, we introduce PointAMaLR, a novel self-supervised learning framework that enhances feature representation and processing accuracy through attention-guided multi-scale local reconstruction. PointAMaLR implements hierarchical reconstruction across multiple local regions, with lower layers focusing on fine-scale feature restoration while upper layers address coarse-scale feature reconstruction, thereby enabling complex inter-patch interactions. Furthermore, to augment feature representation capabilities, we incorporate a Local Attention (LA) module in the embedding layer to enhance semantic feature understanding. Comprehensive experiments on benchmark datasets ModelNet and ShapeNet demonstrate PointAMaLR's superior accuracy and quality in both classification and reconstruction tasks. Moreover, when evaluated on the real-world dataset ScanObjectNN and the 3D large scene segmentation dataset S3DIS, our model achieves highly competitive performance metrics. These results not only validate PointAMaLR's effectiveness in multi-scale semantic understanding but also underscore its practical applicability in real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
