Distance Fields Accelerated with OpenCL

An important task in any graphical simulation is the collision detection between the objects in the simulation. It is desirable to have a good general method for collision detection with high performance. This thesis describes an implementation of a collision detection method that uses distance fields to detect collisions. This method is quite robust and able to detect collisions between most possible shapes. It is also capable of computing contact data for collisions. A problem with distance fields is that the performance cost for making a distance field is quite extensive. It is therefore customary to have some way of accelerating the computation of the distance field (usually by only computing select parts of the field). The application implemented in this thesis solves this performance problem by using the parallel framework OpenCL for accelerating the construction of the field.OpenCL enables programmers to execute code on the GPU. The GPU is highly data parallel and a huge increase in performance can be obtained by letting the GPU handle the computations associated with the initiation of the field