We explore the problem of decoupling color information from geometry in large scenes of voxelized surfaces and of compressing the array of colors without introducing disturbing artifacts. In this extension of our I3D paper with the same title [1] , we first present a novel method for connecting each node in a sparse voxel DAG to its corresponding colors in a separate 1D array of colors, with very little additional information stored to the DAG. Then, we show that by mapping the 1D array of colors onto a 2D image using a space-filling curve, we can achieve high compression rates and good quality using conventional, modern, hardware-accelerated texture compression formats such as ASTC or BC7. We additionally explore whether this method can be used to compress voxel colors for off-line storage and network transmission using conventional off-line compression formats such as JPG and JPG2K. For real-time decompression, we suggest a novel variable bitrate block encoding that consistently outperforms previous work, often achieving two times the compression at equal quality.

We explore the problem of decoupling color information from geometry in large scenes of voxelized surfaces and of compressing the array of colors without introducing disturbing artifacts. First, we present a novel method for connecting each node in a sparse voxel DAG to its corresponding colors in a separate 1D array of colors, with very little additional information stored to the DAG. Then, we show that by mapping the 1D array of colors onto a 2D image using a space-filling curve, we can achieve high compression rates and good quality using conventional, modern, hardware-accelerated texture compression formats such as ASTC or BC7. We additionally explore whether this method can be used to compress voxel colors for off-line storage and network transmission using conventional off-line compression formats such as JPG and JPG2K. For real-time decompression, we suggest a novel variable bitrate block encoding that consistently outperforms previous work, often achieving two times the compression at equal quality.

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.

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.

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. 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.

Recently, several algorithms have been introduced that enable real-time performance for many lights in applications such as games. In this paper, we explore the use of hardware-supported virtual cube-map shadows to efficiently implement high-quality shadows from hundreds of light sources in real time and within a bounded memory footprint. In addition, we explore the utility of ray tracing for shadows from many lights and present a hybrid algorithm combining ray tracing with cube maps to exploit their respective strengths. Our solution supports real-time performance with hundreds of lights in fully dynamic high-detail scenes.

Recently, several algorithms have been introduced that enable real-time performance for many lights in applications such as games. In this paper, we explore the use of hardware-supported virtual cube-map shadows to efficiently implement high-quality shadows from hundreds of light sources in real time and within a bounded memory footprint. In addition, we explore the utility of ray tracing for shadows from many lights and present a hybrid algorithm combining ray tracing with cube maps to exploit their respective strengths. Our solution supports real-time performance with hundreds of lights in fully dynamic high-detail scenes.

Rendering pixel-accurate shadows in scenes lit by a point light-source in real time is still a challenging problem. For scenes of moderate complexity, algorithms based on Shadow Volumes are by far the most efficient in most cases, but traditionally, these algorithms struggle with views where the volumes generate a very high depth complexity. Recently, a method was suggested that alleviates this problem by testing each individual triangle shadow volume against a hierarchical depth map, allowing volumes that are in front of, or behind, the rendered view samples to be efficiently culled. In this paper, we show that this algorithm can be greatly improved by building a full 3D acceleration structure over the view samples and testing per-triangle shadow volumes against that. We show that our algorithm can elegantly maintain high frame-rates even for views with very high-frequency depth-buffers where previous algorithms perform poorly. Our algorithm also performs better than previous work in general, making it, to the best of our knowledge, the fastest pixel-accurate shadow algorithm to date. It can be used with any arbitrary polygon soup as input, with no restrictions on geometry or required pre-processing, and trivially supports transparent and textured shadow-casters.

Producing high-quality shadows in large environments is an important and challenging problem for real-time applications such as games. We propose a novel data structure for precomputed shadows, which enables high-quality filtered shadows to be reconstructed for any point in the scene. We convert a high-resolution shadow map to a sparse voxel octree, where each node encodes light visibility for the corresponding voxel, and compress this tree by merging common subtrees. The resulting data structure can be many orders of magnitude smaller than the corresponding shadow map. We also show that it can be efficiently evaluated in real time with large filter kernels.

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.