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- W3100688879 abstract "Aiming at drastic speedup for point-feature embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptrons (MLP) and a lookup table (LUT) to transform point-coordinate inputs into high-dimensional features. When compared with PointNet's feature embedding part realized by MLP that requires millions of dot products, the proposed framework at test time requires no such layers of matrix-vector products but requires only looking up the nearest entities from the tabulated MLP followed by interpolation, defined over discrete inputs on a 3D lattice that is substantially arranged irregularly. We call this framework LUTI-MLP: LUT Interpolation ML that provides a way to train end-to-end irregularly tabulated MLP coupled to a LUT in a specific manner without the need for any approximation at test time. LUTI-MLP also provides significant speedup for Jacobian computation of the embedding function wrt global pose coordinate on Lie algebra $mathfrak{se}(3)$ at test time, which could be used for point-set registration problems. After extensive evaluation using the ModelNet40, we confirmed that the LUTI-MLP even with a small (e.g., $4^3$) lattice yields performance comparable to that of the MLP while achieving significant speedup: $100times$ for the embedding, $12times$ for the approximate Jacobian, and $860times$ for the canonical Jacobian." @default.
- W3100688879 created "2020-11-23" @default.
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- W3100688879 date "2020-11-12" @default.
- W3100688879 modified "2023-10-18" @default.
- W3100688879 title "Irregularly Tabulated MLP for Fast Point Feature Embedding" @default.
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- W3100688879 doi "https://doi.org/10.48550/arxiv.2011.09852" @default.
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