Compact Precomputed Voxelized Shadows Construction on GPU

We consider the problem of producing high-quality shadows in real-time for 3D computer graphics software. In [1, 4] authors have proposed new data structure for object geometry representation by binary voxel grid. This binary data was packed to directed acyclic graph — traditional sparse voxel octree with merged identical subtrees. This approach has been extended to shadowing by voxelizing shadow volumes instead of object geometry [2, 3]. Obtained structure enables high-quality filtered shadows to be reconstructed for any point in the scene in real-time. In [1–4] authors have used CPU-based bottom-up algorithm that reduces sparse voxel octree to minimal directed acyclic graph. In the present paper we construct new parallel algorithm for such reduction that runs entirely on GPU

Sparse Voxel DAGs for Shadows and for Geometry with Colors

Triangles are probably the most common format for shapes in computer graphics. Nevertheless, when high detail is desired, Sparse Voxel Octrees (SVO) and Sparse Voxel Directed Acyclic Graphs (DAG) can be considerably more memory efficient. One of the first practical use cases for DAGs was to use the structure to represent precomputed shadows. However, previous methods were very time consuming in building the DAG and did not support any other attributes than discretized geometry. Furthermore, when used for scene object representation, the DAGs lacked proper support for properties such as object colors. The focus on this thesis is to speed up the build times of the DAG and to allow other, important, attributes such as colors to be encoded. This thesis is a collection of three papers where we in Paper I solve the problem with slow construction times while also further compressing the DAG, allowing much faster feedback to an\ua0 artist making changes to a scene and also opening up the possibility to recompute the DAG in run time for slowly moving shadows. If a unique color per voxel is desired, which uncompressed would require 3 bytes per voxel, we realize that the benefit from compressing the geometry (down to or even below one bit per voxel) is rendered practically useless. We thus need to find a way to compress the colors as well. In Paper IIA, we solve this issue by mapping the voxel colors to a texture, allowing for the use of conventional compression algorithms, as well as a novel format designed for real-time\ua0 performance. In Paper IIB, we further significantly improve the compression

On sparse voxel DAGs and memory efficient compression of surface attributes for real-time scenarios

The general shape of a 3D object can expeditiously be represented as, e.g., triangles or voxels, while smaller-scale features usually are parameterized over the surface of the object. Such features include, but are not limited to, color details, small-scale surface-normal variations, or even view-dependent properties required for the light-surface interactions. This thesis is a collection of four papers that focus on new ways to compress and efficiently utilize surface data in 3D for real-time usage.In Paper IA and IB, we extend upon the concept of sparse voxel DAGs, a real-time compression format of a voxel-grid, to allow an attribute mapping with a negligible impact on the size. The main contribution, however, is a novel real-time compression format of the mapped colors over such sparse voxel surfaces.Paper II aims to utilize the results of the previous papers to achieve uv-free texturing of surfaces, such as triangle meshes, with optimized run-time minification as well as magnification filtering.Paper III extends upon previous compact representations of view dependent radiance using spherical gaussians (SG). By using a convolutional neural network, we are able to compress the light-field by finding SGs with free directions, amplitudes and sharpnesses, whereas previous methods were limited to only free amplitudes in similar scenarios

Fast, Memory-Efficient Construction of Voxelized Shadows

We present a fast and memory efficient algorithm for generatingCompact Precomputed Voxelized Shadows. By performing much ofthe common sub-tree merging before identical nodes are ever created,we improve construction times by several orders of magnitude forlarge data structures, and require much less working memory. Wealso propose a new set of rules for resolving undefined regions,which significantly reduces the final memory footprint of the alreadyheavily compressed data structure. Additionally, we examine thefeasibility of using CPVS for many local lights and present twoimprovements to the original algorithm that allow us to handlehundreds of lights with high-quality, filtered shadows at real-timeframe rates

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