Hrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin, Damon Conover, Aniket Bera · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28598 · open-access PDF
Las representaciones implícitas neuronales proporcionan parametrizaciones de campo de distancia firmado (SDF) con eficiencia de memoria para escenas LiDAR a gran escala, pero la geometría recuperada de las entradas escasas y a menudo ruidosas lleva a la agitación y los artefactos. Las técnicas clásicas de optimización geométrica resuelven tales artefactos con funciones de energía superficial, cuyos minimizadores corresponden a superficies lisas. Motivados por este principio, formulamos el mapeo neural LiDAR como la optimización de las funciones de energía en el espacio de parámetros de las representaciones implícitas neuronales.
Machine translation (Meta M2M100); the English original follows.
Neural implicit representations provide memory-efficient signed distance field (SDF) parameterizations for large-scale LiDAR scenes, but the geometry recovered from sparse and often noisy inputs carries jitter and artifacts. Classical geometric optimization techniques resolve such artifacts with surface energy functionals, whose minimizers correspond to smooth surfaces. Motivated by this principle, we formulate neural LiDAR mapping as the optimization of energy functionals over the parameter space of neural implicit representations. Specifically, we regularize the signed distance field with an
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