OREN: 実時間ユークリッド符号付距離マッピングのための八分木残差ネットワーク
OREN: Octree Residual Network for Real-Time Euclidean Signed Distance Mapping
八分木補間による明示的な事前分布とニューラルネットワーク回帰による暗黙的な残差を組み合わせ、非打ち切りユークリッドSDFを高精度かつ効率的に再構成するハイブリッド手法を提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zhirui Dai, Qihao Qian, Tianxing Fan, Nikolay Atanasov
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Reconstructing signed distance functions (SDFs) from point cloud data benefits many robot autonomy capabilities, including localization, mapping, motion planning, and control. Methods that support online and large-scale SDF reconstruction often rely on discrete volumetric data structures, which affects the continuity and differentiability of the SDF estimates. Neural network methods have demonstrated high-fidelity differentiable SDF reconstruction but they tend to be less efficient, experience catastrophic forgetting and memory limitations in large environments, and are often restricted to truncated SDF. This work proposes OREN, a hybrid method that combines an explicit prior from octree interpolation with an implicit residual from neural network regression. Our method achieves non-truncated (Euclidean) SDF reconstruction with computational and memory efficiency comparable to volumetric methods and differentiability and accuracy comparable to neural network methods. Extensive experiments demonstrate that OREN outperforms the state of the art in terms of accuracy and efficiency, providing a scalable solution for downstream tasks in robotics and computer vision.
