Reg-NF: ニューラルフィールド間の効率的な暗黙的表面レジストレーション
Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields
2つのニューラルフィールド間の相対6自由度変換を、双方向損失と多視点表面サンプリング、SDFを用いて最適化するレジストレーション手法を提案し、新データセットで評価した。
著者: Stephen Hausler, David Hall, Sutharsan Mahendren, Peyman Moghadam
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for robotics applications. However, limited prior methods have investigated registering multiple neural fields by directly utilising these continuous implicit representations. In this paper, we present Reg-NF, a neural fields-based registration that optimises for the relative 6-DoF transformation between two arbitrary neural fields, even if those two fields have different scale factors. Key components of Reg-NF include a bidirectional registration loss, multi-view surface sampling, and utilisation of volumetric signed distance functions (SDFs). We showcase our approach on a new neural field dataset for evaluating registration problems. We provide an exhaustive set of experiments and ablation studies to identify the performance of our approach, while also discussing limitations to provide future direction to the research community on open challenges in utilizing neural fields in unconstrained environments.
