KEYWORDS: Optical properties, Ray tracing, Cameras, Light sources, Light sources and illumination, Mathematical optimization, Monte Carlo methods, Light scattering, 3D modeling
Currently, for most VR/AR applications it is necessary to reconstruct physically-correctly the real world, and the optical properties of objects. In this paper, to solve this problem, we propose to use a parametric model of optical properties, which is a mix of several known models, which has a small number of parameters and correctly simulates the propagation of light in the scene. In addition, based on the parameters of the model, differential rendering of the image is performed, as a result of which the obtained differentials can be further used in the optimizer. The optimizer, in turn, can modify the model parameters to improve the rendering quality and thus bring the proposed model of optical properties closer to the real optical properties of objects.
The method of photon maps allows to efficiently calculate caustic illumination but requires the use of accelerating spatial partitioning structures to store photonic maps, provide quick access to them, and accelerate the collection of illumination. In this paper, we propose to use a combined accelerating structure based on an octree and hash tables. The structure itself represents an octree consisting of multiple levels of hash tables. This structure makes it possible to obtain a high space partitioning adaptability achieved by using tree structure, while providing quick access to the cells of the structure by utilizing the regular hash table structure. The use of hash tables allows access to the cells of the structure arbitrarily speeding up the search and traversal of cells and photons inside the structure. Unlike other regular structures, hash tables allow to exclude empty cells from storing in memory. Using a tree structure allows to further reduce the memory requirement and ensure the adaptability of space partitioning. The paper proposes an algorithm for generating a combined accelerating structure based on a dynamic analysis of the generating structure. As well as algorithms for finding cells and traversing them with a ray, which allow using this structure to accelerate the photon map method when calculating surface and volume light scattering. SIMD operations are used to speed up the hash tables collision resolution. Efficiency comparison of the photon map method using the proposed accelerated structure and similar tree like structures shows the acceleration of the proposed solution for multi-thread computation.
KEYWORDS: Voxels, Point clouds, Light sources and illumination, Cameras, 3D modeling, Image restoration, Computer graphics, 3D image processing, Reconstruction algorithms
For the correct reconstruction of the real-world geometry, we propose an iterative approach that utilizes the method of differential rendering. This method is based on optimizing the parameters of scene geometry objects to approximate the synthesized scene images to their real images obtained, for example, through digital photography. For the optimization methods to work efficiently during rendering, a series of differential scene images corresponding to the increments of individual scene geometry parameters are formed. To construct the differential scene images, it is necessary to create a parametric model of its geometry. Since the scene geometry is usually represented as a triangular mesh, the triangle vertices become the parameters of the model. However, the number of triangles requires enormous computational resources, which may be impractical. We propose the parametric model of scene geometry, based on a combination of approximate voxel and refined triangular mesh representations. In this model, two levels of geometry modification are suggested. At the first level voxels are the directly modifiable parameters. At the second level individual or connected triangles can be modified precisely. The rendering process is divided into two parts. The first part involves calculating the luminance of direct illumination in the limited representation of the scene, solely related to the modified geometry or its shadow area. The second part involves calculating the luminance of indirect illumination. The calculation of differentials is based on the assumption that the luminance of indirect illumination remains almost unchanged under slight geometry modifications. These solutions significantly reduce the computation time of differentials while preserving the physical correctness of the calculation results and enabling the calculation of noise-free increments of differential scene images for a noisy base image.
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