变分分布先验与显著性图回放的融合用于持续三维重建
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction
AI总结：
本文提出一种融合变分先验与显著性图回放的持续三维重建方法，以缓解灾难性遗忘并降低存储开销，实验证明其性能具有竞争力。
摘要：
单图像三维重建是一项研究挑战，其重点是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被遮挡部分。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，我们提出了一种基于持续学习的三维重建方法，其目标是设计一个使用变分先验的模型，使其即使在新类别上训练之后，仍能合理地重建先前见过的类别。变分先验表示抽象形状并抵抗遗忘，而显著性图以较少的内存使用保留物体属性。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性图的经验回放，以捕获全局和独特的物体特征。充分的实验表明，与已有方法相比，无论在定量还是定性方面，都取得了有竞争力的结果。
英文摘要：
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.
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