Exact and Adaptive Signed Distance Fields Computation for
Rigid and Deformable Models on GPUs


Abstract
Most techniques for realtime
construction of a signed distance field, whether on a CPU or GPU, involve
approximate distances. We use a GPU
to build an exact adaptive distance field, constructed from an octree by using the Morton code. We use rectangleswept spheres to construct a bounding
volume hierarchy (BVH) around a triangulated model. To speed up BVH construction, we can use a multiBVH structure to
improve the workload balance between GPU processors. An upper bound on distance to the model provided by the octree itself allows us to reduce the number of BVHs
involved in determining the distances from
the centers of octree nodes at successively lower
levels, prior to an exact distance query involving the remaining BVHs. Distance fields can be constructed 3564 times as
fast as a serial CPU implementation of a similar algorithm, allowing us to simulate a piece of fabric interacting with the
Stanford Bunny at 20 frames per second.  


Video
 


Benchmarking Scenarios
1. Rigid Models:
2. Deformable Models: We linked our
distance field algorithm to a physics
simulation. We inflated the Bunny (69K triangles)
and dropped a cloth (4K vertices)
around it.
In this benchmark, the distance fields are
recomputed for every simulation step. We computed responsive
forces using penetration depth obtained from
the distance field of bunny. We simulated the
response of the cloth against the bunny at
20 frames per second.
RELATED LINKS
Open
Cloth Library: 

