In recent years, the performance of point cloud models has been rapidly improved. However, due to the limited amount of relevant explainability studies, the unreliability and opacity of these black-box models may lead to potential risks in applications where human lives are at stake, e.g. autonomous driving or healthcare. This work proposes a DDPM-based point cloud global explainability method (DAM) that leverages Point Diffusion Transformer (PDT), a novel point-wise symmetric model, with dual-classifier guidance to generate high-quality global explanations. In addition, an adapted path gradient integration method for DAM is proposed, which not only provides a global overview of the saliency maps for point cloud categories, but also sheds light on how the attributions of the explanations vary during the generation process. Extensive experiments indicate that our method outperforms existing ones in terms of perceptibility, representativeness, and diversity, with a significant reduction in generation time. Our code is available at: https://github.com/Explain3D/DAM

翻译：近年来，点云模型的性能得到了快速提升。然而，由于相关可解释性研究数量有限，这些黑箱模型的不确定性与不透明性可能在涉及人类生命安全的场景（如自动驾驶或医疗健康）中带来潜在风险。本文提出了一种基于DDPM的点云全局可解释性方法（DAM），该方法利用新型逐点对称模型——点扩散Transformer（PDT），结合双分类器引导，生成高质量全局解释。此外，我们还提出了一种针对DAM的自适应路径梯度积分方法，该方法不仅能提供点云类别显著图的全局概览，还能揭示解释属性的生成过程变化规律。大量实验表明，本方法在感知性、代表性和多样性方面均优于现有方法，同时显著缩短了生成时间。代码已开源：https://github.com/Explain3D/DAM