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 and empty. 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，但自早期提出以来鲜有改进。本研究探索将神经网络用作包围体的方法。我们的关键发现是：包围这一传统上被视为计算几何的问题，可被重新定义为通过分类学习将空间划分为“占据”与“空置”的问题。这种基于学习的方法在高维空间（如具有复杂查询的动画场景）中具有显著优势，而神经网络在此类场景中表现优异。然而，实现神经包围体需要突破性设计：允许但严格限制误报，同时确保漏报数量严格为零。我们通过动态加权非对称损失函数实现这种紧致且保守的结果。实验表明，与传统方法相比，神经包围体产生的误报数量可减少一个数量级。