  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. In addition,
we propose an extension of our bounding method using early exits that
accelerates query speeds by 25%. We also demonstrate that our approach is
applicable to non-deep learning models that train within seconds. Our project
page is at: https://wenxin-liu.github.io/neural_bounding/.
