3QFP: 三平面四分木とフーリエ特徴位置エンコーディングによる効率的なニューラル暗黙的表面再構成
3QFP: Efficient neural implicit surface reconstruction using Tri-Quadtrees and Fourier feature Positional encoding
三平面四分木とフーリエ特徴位置エンコーディングを組み合わせ、少ないメモリで滑らかな暗黙的表面再構成を実現する手法を提案。
著者: Shuo Sun, Malcolm Mielle, Achim J. Lilienthal, Martin Magnusson
分類: cs.RO
原文アブストラクト
Neural implicit surface representations are currently receiving a lot of interest as a means to achieve high-fidelity surface reconstruction at a low memory cost, compared to traditional explicit representations.However, state-of-the-art methods still struggle with excessive memory usage and non-smooth surfaces. This is particularly problematic in large-scale applications with sparse inputs, as is common in robotics use cases. To address these issues, we first introduce a sparse structure, \emph{tri-quadtrees}, which represents the environment using learnable features stored in three planar quadtree projections. Secondly, we concatenate the learnable features with a Fourier feature positional encoding. The combined features are then decoded into signed distance values through a small multi-layer perceptron. We demonstrate that this approach facilitates smoother reconstruction with a higher completion ratio with fewer holes. Compared to two recent baselines, one implicit and one explicit, our approach requires only 10\%--50\% as much memory, while achieving competitive quality.
