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.

翻译：单图像三维重建是一项研究挑战，重点在于从单视角图像预测三维物体形状。该任务需要大量数据采集以预测形状的可见部分和遮挡部分。此外，基于学习的方法面临着为所有可能类别创建全面训练数据集的困难。为此，我们提出一种基于持续学习的三维重建方法，其目标是利用变分先验设计一个模型，使其即使在训练新类别后仍能合理重建先前见过的类别。变分先验表示抽象形状并抵御遗忘，而显著图则以较少的内存占用保留物体属性。由于存储大量训练数据存在资源限制，这一点至关重要。此外，我们引入了基于显著图的经验回放机制，以捕捉全局和独特的物体特征。详尽的实验表明，与现有方法相比，该方法在定量和定性评估中均取得了具有竞争力的结果。