Newest papers

Neural Implicit Energy Optimization for LiDAR Mapping

Hrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin, Damon Conover, Aniket Bera · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28598 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ニューラル暗示表示は、メモリ効率的なサインディストリートフィールド(SDF)パラメータ化を大規模なLiDARシーンに提供しますが、稀少でしばしば騒々しい入力から回収されたジオメトリックは、ジッターとアーティファクトを持ちます。古典的なジオメトリック最適化技術は、表面エネルギー機能でこのようなアーティファクトを解決します、その最小化者は滑らかな表面に対応します。この原則によって動機づけられ、私たちは、パラメーター空間上の神経暗示表示のパラメータ上のエネルギー機能の最適化としてニューラルLiDARマッピングを定義します。具体的には、サインディストリートフィールドを、

Machine translation (Meta M2M100); the English original follows.

Abstract

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 energy functional defined on the shape operator of its level set, which represents the curvature of the surface, thus encouraging smoothness along the surface. This adapts classical geometric priors to the scale of million-point LiDAR maps. Experiments on large-scale datasets show reconstructions with improved smoothness at preserved geometric fidelity, and no additional inference-time cost. The code is publicly available at \href{https://github.com/HrishikeshVish/nieo}{https://github.com/HrishikeshVish/nieo}

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Lidar, Smoothness, Energy (signal processing), Curvature, Signed distance function, Artificial neural network

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange