Finding Efficient Spatial Distributions for Massively Instanced 3-d Models
Stefan Zellmann, Nate Morrical, Ingo Wald, Valerio Pascucci
accepted at EGPGV 2020.
Instancing is commonly used to reduce the memory footprint of massive 3-d models. Nevertheless, large production assets often do not fit into the memory allocated to a single rendering node or into the video memory of a single GPU. For memory intensive scenes like these, distributed rendering can be helpful. However, finding efficient data distributions for these instanced 3-d models is challenging, since a memory-efficient data distribution often results in an inefficient spatial distribution, and vice versa. Therefore, we propose a k-d tree construction algorithm that balances these two opposing goals and evaluate our scene distribution approach using publicly available instanced 3-d models like Disney’s Moana Island Scene.