自己組織化ガウス混合モデルによる確率的点群モデリング
Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models
情報理論学習の自己組織化原理を用いて、シーン複雑度に応じてガウス混合モデルの成分数を自動調整し、点群データを連続確率的にモデル化する手法を提案した論文。
著者: Kshitij Goel, Nathan Michael, Wennie Tabib
分類: cs.LG, cs.GR, cs.RO
原文アブストラクト
This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of balancing model fidelity with size. Instead, state-of-the-art mapping approaches require tuning parameters for specific use cases, but do not generalize across diverse environments. To address this gap, we utilize a self-organizing principle from information-theoretic learning to automatically adapt the complexity of the GMM model based on the relevant information in the sensor data. The approach is evaluated against existing point cloud modeling techniques on real-world data with varying degrees of scene complexity.
