  We propose a neural network-based approach for collision detection with
deformable objects. Unlike previous approaches based on bounding volume
hierarchies, our neural approach does not require an update of the spatial data
structure when the object deforms. Our network is trained on the reduced
degrees of freedom of the object, so that we can use the same network to query
for collisions even when the object deforms. Our approach is simple to use and
implement, and it can readily be employed on the GPU. We demonstrate our
approach with two concrete examples: a haptics application with a finite
element mesh, and cloth simulation with a skinned character.
