The use of deep 3D point cloud models in safety-critical applications, such as autonomous driving, dictates the need to certify the robustness of these models to semantic transformations. This is technically challenging as it requires a scalable verifier tailored to point cloud models that handles a wide range of semantic 3D transformations. In this work, we address this challenge and introduce 3DCertify, the first verifier able to certify robustness of point cloud models. 3DCertify is based on two key insights: (i) a generic relaxation based on first-order Taylor approximations, applicable to any differentiable transformation, and (ii) a precise relaxation for global feature pooling, which is more complex than pointwise activations (e.g., ReLU or sigmoid) but commonly employed in point cloud models. We demonstrate the effectiveness of 3DCertify by performing an extensive evaluation on a wide range of 3D transformations (e.g., rotation, twisting) for both classification and part segmentation tasks. For example, we can certify robustness against rotations by $\pm60^\circ$ for 95.7% of point clouds, and our max pool relaxation increases certification by up to 15.6%.
翻译:在安全关键应用(如自主驾驶)中,使用深 3D点云模型在安全关键应用中,如自动驾驶,意味着需要验证这些模型对于语义变异的稳健性。这在技术上具有挑战性,因为它要求有一个可扩缩的核查器,用于点云模型,处理广泛的语义3D变异。在这项工作中,我们应对这一挑战并引入3DCertic,这是第一个能够验证点云模型的稳健性的核查器。 3DCertic 以两个主要见解为基础:(一) 基于适用于任何不同变异的一阶泰勒近似值的一般放松,以及(二) 精确地放松全球地物集合,因为它比点感激活(如ReLU或sigmoid)更为复杂,但通常用于点云模型。我们通过对3D变异变(如旋转、扭动)的广泛评价,证明3Derticriferti的有效性,用于分类和部分分解任务。例如,我们可以通过 $\p60 irrcrc_rc_rus crowdroup by 15rendendrencerence_rence_rence_rus liver_rus colver_liver_ligilation_liculation_lic_lic_95.6._rc_xxlum_xlusxxxlup_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxlxlxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx