Browsing by Author "PEDRO, ERENDIRO SANGUEVE NJUNJUVILI"
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- Tetrahedron-Tetrahedron intersection and volume computation using neural networksPublication . PEDRO, ERENDIRO SANGUEVE NJUNJUVILI; Ramos, Carlos Fernando da SilvaThis thesis introduces a framework for fast, learning-based analysis of tetrahedron-tetrahedron interactions, combining scalable dataset generation with an efficient neural model. At its core is TetrahedronPairDatasetV1, a curated collection of one million labeled tetrahedron pairs with ground truth intersection status and volumes, filling a longstanding gap in geometry learning. Built on this dataset, we present TetrahedronPairNet, a neural architecture that adapts PointNet and DeepSets for processing tetrahedron pairs. The model simultaneously predicts intersection classification and intersection volume, achieving real-time performance: over 98% classification accuracy and a mean absolute error of ≈ 0.0012 in volume estimation (R2 = 0.68). It processes over 30,000 samples per second with full preprocessing—orders of magnitude faster than classical algorithms. Unlike traditional symbolic approaches, TetrahedronPairNet is robust to degenerate configurations and requires no handcrafted geometry logic. Its fully batched, differentiable design supports seamless integration into simulation pipelines, CAD tools, and learning-based physics engines. This work reframes geometric intersection as a data-driven inference task, laying the foundation for scalable, real-time, and intelligent geometry processing across computational design, simulation, AR/VR, and scientific computing.