Decomposition is a technique commonly used to partition complex
models into simpler components.
While decomposition into convex components
results in pieces that are easy to process, such
decompositions can be costly to construct and can result in
representations with an unmanageable number of components.
In this paper, we explore an alternative partitioning strategy that
decomposes a given model into ``approximately convex'' pieces
that may provide similar benefits as convex components, while
the resulting decomposition is both significantly smaller (typically
by orders of magnitude) and can be computed more efficiently.
Indeed, for many applications, an approximate convex decomposition (ACD)
can more accurately represent the important structural features of the model
by providing a mechanism for ignoring less significant features,
such as surface texture.
We describe a technique for computing ACDs of three-dimensional
polyhedral solids and surfaces of arbitrary genus.
We provide results illustrating that our approach results in high
quality decompositions with very few components and applications showing
that comparable or better results can be obtained using ACD decompositions
in place of exact convex decompositions (ECD) that are several orders
of magnitude larger.