Efficient Penetration Depth Computation between Rigid Models using Contact Space Propagation Sampling

TL;DR

This paper introduces a fast, high-quality method for approximating the global penetration depth between complex non-convex models by precomputing contact space using a novel sampling technique, enabling efficient run-time queries.

Contribution

The paper proposes a new propagation sampling algorithm for contact space approximation, significantly improving precomputation speed and accuracy over previous random sampling methods.

Findings

Significant accuracy improvements over prior methods.

Precomputation is faster with high-quality contact space approximation.

Efficient run-time queries for penetration depth on complex models.

Abstract

We present a novel method to compute the approximate global penetration depth (PD) between two non-convex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximation of the high-dimensional contact space between the pair of models. As compared with prior random sampling algorithms for contact space approximation, our propagation sampling considerably speeds up the precomputation and yields a high quality approximation. At run-time, we perform a nearest-neighbor query and local projection to efficiently compute the translational or generalized PD. We demonstrate the performance of our approach on complex 3D benchmarks with tens or hundreds of thousands of triangles, and we observe significant improvement over previous methods in terms of accuracy, with a modest improvement in the run-time performance.