Residency Octree: A Hybrid Approach for Scalable Web-Based Multi-Volume Rendering

TL;DR

The paper introduces Residency Octree, a hybrid multi-volume rendering method that decouples data resolution from spatial subdivision, enabling efficient, flexible, and scalable web-based visualization of large multi-volume datasets.

Contribution

It proposes a novel Residency Octree structure that allows mixed-resolution out-of-core rendering with fine-grained empty-space skipping, improving speed and flexibility over prior methods.

Findings

Faster rendering compared to previous approaches.

Supports adaptive resolution mixing and sampling.

Efficient for multiple data channels in web-based environments.

Abstract

We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.