Point Cloud Voxelization

Point Cloud Voxelization

Large point cloud visualization is challenging because of the huge sizes of modern data sets. Additionally, using points as rendering primitives has significant drawbacks, that are further amplified in situations where large numbers of points are involved. Put simply, mathematical points have no volume, and, therefore do not cast shadows. Further, points have no surface area and no natural normal vector, which is commonly used for shading. Although, both volume and surface area can be approximated, it is difficult to get it right. An alternative is to use cubes as rendering primitives. Cubes have clearly defined volumes and six distinct surfaces, which means they can be rendered with traditional techniques.

Cubes are created and stored in a hierarchical data structure known as an octree. Using a divide-and-conquer strategy, cubes are created to encapsulate the input point set. So, for each cube there is one or more data points inside its volume. The cubes are in fact the bounding boxes of the leaf nodes in the octree, which is recursively sub-divided to a user-defined depth. Data points are streamed, which means that there is no limit to the number of points being used in octree creation. Further, the memory footprint of the octree is orders of magnitude smaller than that of the raw points. The cubes are output to an OBJ-file, which can be rendered in real-time or offline. In the case of offline rendering (especially ray-tracing) it is possible to merge neighboring cube faces, to drastically reduce the amount of geometry to process (approx. an order of magnitude). This greatly speeds up rendering times without compromising visual quality. Images on the left were rendered with Maxwell.

Digital Dublin/VoxelVille

The image below was shortlisted for the UCD Image of Research Competition 2008. The input point set for this image was part of a high-grade aerial laser scan of the city of Dublin.


Radiohead – House of Cards

Famous rockband Radiohead have generously released the data captured during the making of the video for their single House of Cards. The data consists of several thousand frames of real-time laser scan data of singer Thom Yorke’s face. A few frames are showed on the left. Two levels of voxelization, rendered in different colors, are overlaid and rendered with depth-of-field and motion blur. Finally, the images were post-processed to remove some saturation from the green and blue channels.



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