Exact and Adaptive Signed Distance Fields

Computation for Rigid and Deformable Models on GPUs

Abstract

Most techniques for real-time 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 rectangle-swept spheres to construct a bounding volume hierarchy (BVH) around a triangulated model. To speed up BVH construction, we can use a multi-BVH 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 35-64 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:

We applied our GPU-based parallel algorithm to six geometric models, and generated adaptive distance fields with resolutions from 8^3 to 1024^3. We compared our GPU approach to a CPU implementation using the PQP proximity library. Our GPU implementation is 35 to 64 faster.

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:

https://code.google.com/p/opencloth/