Distributed systems such as clusters of PCs are low-cost alternatives for running parallel rendering systems, but they have high communication overhead, and limited memory capacity on each processing node. In this paper, we focus on the strategy for distributing the parallel rendering work among the PCs. A good distribution strategy provides better load balance, and avoids the need for replicating data on the relatively small memory of each PC. Our goal is to study different distribution strategies on the scope of the Parallel ZSweep algorithm, introducing in PZSweep another work distribution strategy: work stealing. This strategy allows a decentralized control of the work to be done, and provides a dynamic load redistribution. We propose two different algorithms to select the processor that will be "stolen" and show that the simplest one, nearest neighbor, was the most efficient. We also showed that the load redistribution schemes strongly depended on the initial load distribution, with an interleaved assignment, our systems could outperform the original Parallel ZSweep algorithm. We conclude that for running large datasets on a cluster of PCs, Parallel ZSweep requires dynamic load distribution strategy.