Eq. (15) has been derived for perturbations of thetrainingdata. At this point we have a choice of how to adversarially perturb the classifier to achieve the largest effectonthenetworkoutput. Then, with similar reasoning that led to Eq.(12)wenowobtain: When we measure quantities from the neural net, we subtract the initialpredictionf0,since the NTK expression Eq.(3) does not take the initialization of the network into account. D.2 AdditionalPlots Complementing Figure 1 in the main text, we show (the first 100) NTK features in Robustness UsefulnessspacedefinedinSec.

In this paper, we propose a novel design ofSparse Steerable Convolution (SS-Conv)toaddress theshortcoming; SS-Convgreatly accelerates steerable convolution with sparse tensors, while strictly preserving the property of SE(3)-equivariance. Based on SS-Conv, we propose a general pipeline for precise estimation of object poses, wherein a key design is a Feature-Steering module that takes the full advantage of SE(3)-equivariance and is able to conduct an efficient pose refinement.