Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception. In this work, we study the use of neural networks as bounding volumes. Our key observation is that bounding, which so far has primarily been considered a problem of computational geometry, can be redefined as a problem of learning to classify space into free or occupied. This learning-based approach is particularly advantageous in high-dimensional spaces, such as animated scenes with complex queries, where neural networks are known to excel. However, unlocking neural bounding requires a twist: allowing -- but also limiting -- false positives, while ensuring that the number of false negatives is strictly zero. We enable such tight and conservative results using a dynamically-weighted asymmetric loss function. Our results show that our neural bounding produces up to an order of magnitude fewer false positives than traditional methods.

翻译：包围体是计算机图形学和视觉任务中的一个经典概念，但自其早期提出以来几乎没有太大变化。在这项工作中，我们研究了将神经网络作为包围体的应用。我们的核心观察是：迄今主要被视为计算几何问题的包围问题，可以重新定义为学习将空间分类为自由或占用的学习问题。这种基于学习的方法在高维空间中尤其具有优势，例如涉及复杂查询的动画场景，而神经网络正擅长处理此类场景。然而，实现神经包围体需要一个关键转变：允许（但也要限制）假阳性，同时确保假阴性数量严格为零。我们通过一种动态加权的非对称损失函数实现了这种紧致且保守的结果。实验结果表明，我们的神经包围体产生的假阳性数量比传统方法低一个数量级。