Abstract:The perturbation features limit the performance of point cloud classification models both for clean point cloud and point cloud obtained from real-world. In this paper, we propose two methods to enhance models by reducing the negative impact of the nuisance features from the perspective of interpretability, namely, dropping the nuisance points before inputting the point cloud into models and adaptively selecting the important features during the training process. The former is achieved by saliency analysis of … Show more
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