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

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

UV-free Texturing using Sparse Voxel DAGs

An application may have to load an unknown 3D model and, for enhanced realistic rendering, precompute values over the surface domain, such as light maps, ambient occlusion, or other global-illumination parameters. High-quality uv-unwrapping has several problems, such as seams, distortions, and wasted texture space. Additionally, procedurally generated scene content, perhaps on the fly, can make manual uv unwrapping impossible. Even when artist manipulation is feasible, good uv layouts can require expertise and be highly labor intensive. This paper investigates how to use Sparse Voxel DAGs (or DAGs for short) as one alternative to avoid uv mapping. The result is an algorithm enabling high compression ratios of both voxel structure and colors, which can be important for a baked scene to fit in GPU memory. Specifically, we enable practical usage for an automatic system by targeting efficient real-time mipmap filtering using compressed textures and adding support for individual mesh voxelizations and resolutions in the same DAG. Furthermore, the latter increases the texture-compression ratios by up to 32% compared to using one global voxelization, DAG compression by 10 – 15% compared to using a DAG per mesh, and reduces color-bleeding problems for large mipmap filter sizes. The voxel-filtering is more costly than standard hardware 2D-texture filtering. However, for full HD with deferred shading, it is optimized down to 2.5 \ub1 0.5 ms for a custom multisampling filtering (e.g., targeted for minification of low-frequency textures) and 5 \ub1 2 ms for quad-linear mipmap filtering (e.g., for high-frequency textures). Multiple textures sharing voxelization can amortize the majority of this cost. Hence, these numbers involve 1–3 textures per pixel (Fig. 1c)

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

Compressing color data for voxelized surface geometry

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

Compressing color data for voxelized surface geometry

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

Spherical Gaussian Light‐field Textures for Fast Precomputed Global Illumination

We describe a method to use Spherical Gaussians with free directions and arbitrary sharpness and amplitude to approximate the precomputed local light field for any point on a surface in a scene. This allows for a high‐quality reconstruction of these light fields in a manner that can be used to render the surfaces with precomputed global illumination in real‐time with very low cost both in memory and performance. We also extend this concept to represent the illumination‐weighted environment visibility , allowing for high‐quality reflections of the distant environment with both surface‐material properties and visibility taken into account. We treat obtaining the Spherical Gaussians as an optimization problem for which we train a Convolutional Neural Network to produce appropriate values for each of the Spherical Gaussians\u27 parameters. We define this CNN in such a way that the produced parameters can be interpolated between adjacent local light fields while keeping the illumination in the intermediate points coherent