Hrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin, Damon Conover, Aniket Bera · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28598 · open-access PDF
神经间接表示为大型 LiDAR 场景提供记忆效率高达签名距离场(SDF)参数化,但从稀缺和常常噪音的输入中恢复的几何形状带来了动摇和文物。经典的几何优化技术用表面能量功能来解决这些文物,其最小化剂与平滑表面相匹配。 根据这一原则,我们制定神经 LiDAR 地图作为神经间接表示参数空间上的能量功能的优化。
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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