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One approach to the broad phase, as mentioned, is the sort and sweep algorithm (Witkin and Baraff 1997). Our CUDA implementation of parallel spatial subdivision is described in Section 32.2. In Section 32.1.3, we outline the complications arising from a parallel implementation of spatial subdivision and how we deal with them. Alternative algorithms, such as sort and sweep(described in Section 32.1.1) or spatial subdivision (described in Section 32.1.2), achieve an average complexity of O( n log n) (their worst-case complexity remains O( n 2)). The brute-force implementation of broad phase for n objects consists in conducting n( n-1)/2 collision tests. In this chapter, we present a GPU implementation of broad-phase collision detection ("broad phase," for short) based on CUDA that is an order of magnitude faster than the CPU implementation. Such an approach is very suitable when a lot of the objects are actually not colliding-as is the case in practice-so that many pairs of objects are rejected during the broad phase. In the narrow phase, collision tests are exact-they usually compute exact contact points and normals-thus much more costly, but performed for only the pairs of the potentially colliding set. The output of the broad phase is the potentially colliding set of pairs of objects. In the broad phase, collision tests are conservative-usually based on bounding volumes only-but fast in order to quickly prune away pairs of objects that do not collide with each other. Most efficient implementations use a two-phase approach: a broad phase followed by a narrow phase. Broad-Phase Collision Detection with CUDAĬollision detection among many 3D objects is an important component of physics simulation, computer-aided design, molecular modeling, and other applications. You can also subscribe to our Developer News Feed to get notifications of new material on the site.Ĭhapter 32. The CD content, including demos and content, is available on the web and for download.
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