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3D object classification has attracted appealing attentions in academicresearches and industrial applications. However, most existing methods need toaccess the training data of past 3D object classes when facing the commonreal-world scenario: new classes of 3D objects arrive in a sequence. Moreover,the performance of advanced approaches degrades dramatically for past learnedclasses (i.e., catastrophic forgetting), due to the irregular and redundantgeometric structures of 3D point cloud data. To address these challenges, wepropose a new Incremental 3D Object Learning (i.e., I3DOL) model, which is thefirst exploration to learn new classes of 3D object continually. Specifically,an adaptive-geometric centroid module is designed to construct discriminativelocal geometric structures, which can better characterize the irregular pointcloud representation for 3D object. Afterwards, to prevent the catastrophicforgetting brought by redundant geometric information, a geometric-awareattention mechanism is developed to quantify the contributions of localgeometric structures, and explore unique 3D geometric characteristics with highcontributions for classes incremental learning. Meanwhile, a score fairnesscompensation strategy is proposed to further alleviate the catastrophicforgetting caused by unbalanced data between past and new classes of 3D object,by compensating biased prediction for new classes in the validation phase.Experiments on 3D representative datasets validate the superiority of our I3DOLframework.

I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting