Exploiting coherence in time-varying voxel data

We encode time-varying voxel data for efficient storage and streaming. We store the equivalent of a separate sparse voxel octree for each frame, but utilize both spatial and temporal coherence to reduce the amount of memory needed. We represent the time-varying voxel data in a single directed acyclic graph with one root per time step. In this graph, we avoid storing identical regions by keeping one unique instance and pointing to that from several parents. We further reduce the memory consumption of the graph by minimizing the number of bits per pointer and encoding the result into a dense bitstream

Photon Splatting Using a View-Sample Cluster Hierarchy

Splatting photons onto primary view samples, rather than gathering from a photon acceleration structure, can be a more efficient approach to evaluating the photon-density estimate in interactive applications, where the number of photons is often low compared to the number of view samples. Most photon splatting approaches struggle with large photon radii or high resolutions due to overdraw and insufficient culling. In this paper, we show how dynamic real-time diffuse interreflection can be achieved by using a full 3D acceleration structure built over the view samples and then splatting photons onto the view samples by traversing this data structure. Full dynamic lighting and scenes are possible by tracing and splatting photons, and rebuilding the acceleration structure every frame. We show that the number of view-sample/photon tests can be significantly reduced and suggest further culling techniques based on the normal cone of each node in the hierarchy. Finally, we present an approximate variant of our algorithm where photon traversal is stopped at a fixed level of our hierarchy, and the incoming radiance is accumulated per node and direction, rather than per view sample. This improves performance significantly with little visible degradation of quality

High Resolution Sparse Voxel DAGs

Figure 1: The EPICCITADEL scene voxelized to a 128K 3 (131 072 3) resolution and stored as a Sparse Voxel DAG. Total voxel count is 19 billion, which requires 945MB of GPU memory. A sparse voxel octree would require 5.1GB without counting pointers. Primary shading is from triangle rasterization, while ambient occlusion and shadows are raytraced in the sparse voxel DAG at 170 MRays/sec and 240 MRays/sec respectively, on an NVIDIA GTX680. We show that a binary voxel grid can be represented orders of magnitude more efficiently than using a sparse voxel octree (SVO) by generalising the tree to a directed acyclic graph (DAG). While the SVO allows for efficient encoding of empty regions of space, the DAG additionally allows for efficient encoding of identical regions of space, as nodes are allowed to share pointers to identical subtrees. We present an efficient bottom-up algorithm that reduces an SVO to a minimal DAG, which can be applied even in cases where the complete SVO would not fit in memory. In all tested scenes, even the highly irregular ones, the number of nodes is reduced by one to three orders of magnitude. While the DAG requires more pointers per node, the memory cost for these is quickly amortized and the memory consumption of the DAG is considerably smaller, even when compared to an ideal SVO without pointers. Meanwhile, our sparse voxel DAG requires no decompression and can be traversed very efficiently. We demonstrate this by ray tracing hard and soft shadows, ambient occlusion, and primary rays in extremely high resolution DAGs at speeds that are on par with, or even faster than, state-of-the-art voxel and triangle GPU ray tracing

Fast, Memory-Efficient Construction of Voxelized Shadows

We present a fast and memory efficient algorithm for generating Compact Precomputed Voxelized Shadows. By performing much of the common sub-tree merging before identical nodes are ever created, we improve construction times by several orders of magnitude for large data structures, and require much less working memory. To further improve performance, we suggest two new algorithms with which the remaining common sub-trees can be merged. We also propose a new set of rules for resolving undefined regions, which significantly reduces the final memory footprint of the already heavily compressed data structure. Additionally, we examine the feasibility of using CPVS for many local lights and present two improvements to the original algorithm that allow us to handle hundreds of lights with high-quality, filtered shadows at real-time frame rates