A Fusion of Variational Distribution Priors and Saliency Map Replay for
  Continual 3D Reconstruction
Sanchar Palit Sandika Biswas
Single-image 3D reconstruction is a research challenge focused on predicting
3D object shapes from single-view images. This task requires significant data
acquisition to predict both visible and occluded portions of the shape.
Furthermore, learning-based methods face the difficulty of creating a
comprehensive training dataset for all possible classes. To this end, we
propose a continual learning-based 3D reconstruction method where our goal is
to design a model using Variational Priors that can still reconstruct the
previously seen classes reasonably even after training on new classes.
Variational Priors represent abstract shapes and combat forgetting, whereas
saliency maps preserve object attributes with less memory usage. This is vital
due to resource constraints in storing extensive training data. Additionally,
we introduce saliency map-based experience replay to capture global and
distinct object features. Thorough experiments show competitive results
compared to established methods, both quantitatively and qualitatively.