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Single-image 3D reconstruction is a research challenge focused on predicting3D object shapes from single-view images. This task requires significant dataacquisition to predict both visible and occluded portions of the shape.Furthermore, learning-based methods face the difficulty of creating acomprehensive training dataset for all possible classes. To this end, wepropose a continual learning-based 3D reconstruction method where our goal isto design a model using Variational Priors that can still reconstruct thepreviously seen classes reasonably even after training on new classes.Variational Priors represent abstract shapes and combat forgetting, whereassaliency maps preserve object attributes with less memory usage. This is vitaldue to resource constraints in storing extensive training data. Additionally,we introduce saliency map-based experience replay to capture global anddistinct object features. Thorough experiments show competitive resultscompared to established methods, both quantitatively and qualitatively.

A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction