Voxel transformation: scalable scene geometry discretization for global illumination

• Published in: Journal of real-time image processing 2020-10, Vol.17 (5), p.1585-1596
• Main Authors: Yalçıner, Bora, Sahillioğlu, Yusuf
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Computer Graphics; Computer Science; Data structures; Discretization; Illumination; Image Processing and Computer Vision; Light; Methods; Multimedia Information Systems; Octrees; Original Research Paper; Pattern Recognition; Signal,Image and Speech Processing; Spatial data; Transformations
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-019-00919-1

In real-time computer graphics, efficient discretization of scenes is required in order to accelerate graphics related algorithms such as realistic rendering with indirect illumination and visibility checking. Sparse voxel octree (SVO) is a popular data structure for such a discretization task. Populating an SVO with data is challenging when dynamic object count is high, especially when data per spatial location is large. Problem of populating such trees is adressed with our Voxel Transformation method, where pre-generated voxel data is transformed from model space to world space on demand, in contrast to the common way of voxelizing each dynamic object over each frame. Additionally, an accompanying filtering technique for voxel transformation is also proposed. This technique serves proposed system in two ways: (1) resolves issues introduced by the proposed fast and scalable voxel transformation method, and (2) enables smooth transitions between frames and handles the aliasing problem naturally as shown in the supplementary video. As an application use case, the proposed Voxel Transformation method is demonstrated in order to achieve indirect illumination using the well-known voxel cone tracing method. Results, which is compared with the standard voxelization method and ground-truth, are visually appealing and also scalable over large number of dynamic objects as shown in the supplementary video.