Boksansky, JakubMeister, DanielBenthin, CarstenKnoll, AaronPeters, Christoph2025-06-202025-06-202025978-3-03868-291-22079-8687https://doi.org/10.2312/hpg.20251175https://diglib.eg.org/handle/10.2312/hpg20251175The encoding of input parameters is one of the fundamental building blocks of neural network algorithms. Its goal is to map the input data to a higher-dimensional space [RBA*19], typically supported by trained feature vectors [MESK22]. The mapping is crucial for the efficiency and approximation quality of neural networks. We propose a novel geometry-aware encoding called GATE that stores feature vectors on the surface of triangular meshes. Our encoding is suitable for neural rendering-related algorithms, for example, neural radiance caching [MRNK21]. It also avoids limitations of previous hash-based encoding schemes, such as hash collisions, selection of resolution versus scene size, and divergent memory access. Our approach decouples feature vector density from geometry density using mesh colors [YKH10], while allowing for finer control over neural network training and adaptive level-of-detail.Attribution 4.0 International License10.2312/hpg.202511759 pages