This research develops an effective and precise collision detection (Cd) algorithm for real-time simulation in virtual environments such as computer graphics, realistic and immersive virtual reality (VR), augmented re...
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This research develops an effective and precise collision detection (Cd) algorithm for real-time simulation in virtual environments such as computer graphics, realistic and immersive virtual reality (VR), augmented reality (AR) and physical-based simulation within an enhanced algorithm for object collision detection in 3d geometry. We describe an improved algorithm through a comparison in the application of a central processing unit (CPU) and graphics processing units (GPU). Although leveraging CPU for computational speed improvements has gained significant recognition in recent years, this study distinguishes by tracking 3d geometry bounding volume hierarchy (BVH) constructed in a spatial decomposition structure with a focus on Octree-based Axis-Aligned Bounding Box (AABB) structure in 3d scene to compute collision detection to swiftly reject disjoint objects and minimize the number of triangle primitives that need to be processed and then the M & ouml;ller method is utilized to compute precise triangle primitives, further enhancing the efficiency and precision of the collision detection process. This approach is also designed to implement computation with GPU which utilizes the high-level shader language (HLSL) programming language on the compute shader unity3d. AABB is structured as the maximum and minimum hexahedron enclosing an object that is parallel to the coordinate axis. Otherwise, GPU computational technique is a crucial method for further enhancing the object's performance. The proposed method utilizes Octree AABB-based GPU parallel processing to reduce the computational load of real-time collision detection simulations and to handle multiple computations simultaneously. Comparative performance evaluations demonstrate that our GPU-accelerated framework consistently reaches the fastest collision detection times from 1.01 to 45.62 times, respectively.
deep learning models performing complex tasks require the support of datasets. With the advancement of virtual reality technology, the use of virtual datasets in deep learning models is becoming more and more widespre...
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deep learning models performing complex tasks require the support of datasets. With the advancement of virtual reality technology, the use of virtual datasets in deep learning models is becoming more and more widespread. Indoor scenes represents a significant area of interest for the application of machine vision technologies. Existing virtual indoor datasets exhibit deficiencies with regard to camera poses, resulting in problems such as occlusion, object omission, and objects having too small of a proportion of the image, and perform poorly in the training for object detection and simultaneous localization and mapping (SLAM) tasks. Aiming at the problems regarding the capacity of cameras to comprehensively capture scene objects, this study presents an enhanced algorithm based on rapidly exploring random tree star (RRT*) for the generation of camera poses in a 3d indoor scene. Meanwhile, in order to generate multimodal data for various deep learning tasks, this study designs an automatic image acquisition module under the unity3d platform. The experimental results from running the model on several mainstream virtual indoor datasets-such as 3d-FRONT and Hypersim-indicate that the image sequences generated in this study show enhancements in terms of object capture rate and efficiency. Even in cluttered environments such as those in SceneNet RGB-d, the object capture rate remains stable at around 75%. Compared with the image sequences from the original datasets, those generated in this study achieve improvements in the object detection and SLAM tasks, with increases of up to approximately 30% in mAP for the YOLOv10 object detection task and up to approximately 10% in SR for the ORB-SLAM algorithm.
In order to improve the efficiency of experimental teaching in colleges and universities and the comprehensive ability of students, this paper constructs a virtual simulation experimental teaching system based on Unit...
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ISBN:
(纸本)9798400711732
In order to improve the efficiency of experimental teaching in colleges and universities and the comprehensive ability of students, this paper constructs a virtual simulation experimental teaching system based on unity3d. It adopts modular architecture design, integrates scene construction, interaction logic, intelligent feedback and evaluation and other key technologies to complete the optimization of teaching content and process implementation. The effectiveness of the system in improving teaching quality and reducing resource consumption is verified through the case of “Mechanics of Materials Experiment”. The study shows that the system not only improves the efficiency of the experiment, but also significantly enhances the practical ability and innovative consciousness of students, which provides theoretical and practical support for the wide application of virtual simulation experiment in education.
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