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Finding Efficient Spatial Distributions for Massively Instanced 3-d Models

Stefan Zellmann, Nate Morrical, Ingo Wald, Valerio Pascucci
accepted at EGPGV 2020.

Preprint (author version)

Source code (link to github repo)


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.