KEYWORDS: Light sources and illumination, Education and training, Monte Carlo methods, Light sources, Reflection, Ray tracing, Spatial resolution, Shadows, Mathematical optimization, 3D modeling
Neural rendering can achieve satisfactory illumination effects with unknown light source locations and brightness. When training on dynamic scenarios (where object positions, textures, lighting, and viewpoints can vary), variations in object position, material properties, light intensity, and angle make the training results unsatisfactory due to the influence of global light sources. This paper proposes a novel global illumination exploration method, which uses Markov Chain Monte Carlo (MCMC) to perform small sample sampling of light points, and incorporates a new Bayesian optimization (Randomized Search) strategy to optimize the sample data combined with real-time data to remove sampling redundant information and improve training efficiency. The comprehensive experimental results show that our proposed method provides a practical and effective solution for the study of global illumination in dynamic scenes.
Virtual-real occlusion is a key step in augmented reality systems. The traditional virtual-real occlusion method has some problems, such as low accuracy and poor adaptation to the scene. On this basis, this paper proposes a virtual real occlusion method based on monocular camera. Firstly, AKAZE algorithm was combined with Tanimoto similarity estimation to find the optimal pair of feature points, and the relative depth of feature points on adjacent frames was calculated by using the triangulation method in SFM algorithm. Then, the real depth value was obtained by combining the translation scale. Finally, the depth value of virtual and real objects was compared to realize the virtual and real occlusion of monocular camera. The experimental results show that the virtual-real occlusion based on monocular camera is realized and the occlusion accuracy is high.
The fisheye camera is widely used in computer vision because of its large field of view. However, in optical theory, the large field of view is at the cost of distortion. It is impossible to obtain effective information directly from the fisheye image, so the original image needs to be distorted first to become a linear image without distortion. Aiming at the existence of traditional longitude correction in the image center, edge correction effect is different and edge distortion, introducing the repositioning center algorithm and stretch factor. Firstly, the effective area of fisheye image is obtained by row-by-row column scanning method. And then uses the repositioning center algorithm to obtain the new center and radius, according to the distortion principle to calculate the distortion principle. The mapping relationship between the target image and the original image is obtained, and finally modified by the bilinear interpolation method. Compared with the traditional longitude correction, the proposed algorithm can correct the fisheye image accurately and effectively and improve the quality of correction.
Image matching is one of the hot issues in recent years. Image matching determines the effect of image mosaic to a large extent. Therefore, a stable, accurate, and fast image matching algorithm is very important for the subsequent processing of image information. Most of the traditional image matching methods have the problems of high-error matching rate and difficulty in to eliminating false matching. To resolve the shortcomings listed here, an improved algorithm was proposed in which a combined measure of Hamming distance. Similarity measurement with Tanimoto similarity measurement was adopted to dispose binary feature vectors. In addition, as an important step in the process of image mosaics, the calculation of the best mosaics can eliminate the ghosting and ghosting that may appear in the mosaics. However, the traditional dynamic planning method is easy to pass through the obstacles and form obvious stitching traces when searching for stitching lines in images with large obstacles. In view of the aforementioned content, we propose a fast and robust method to search the image difference matrix and structure difference matrix of the overlapping area of the input image according to the search strategy to determine the pixel coordinates of the splicing line. Finally, from an optimal splicing line, the experimental results show that our algorithm performs well for eliminating error matching points. Compared with traditional algorithms, matching accuracy is improved by 25%. In terms of calculating the optimal splicing line, the method proposed is faster than the traditional method and has good robustness.
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