提出一种实时欧氏符号距离函数重建方法，兼顾精度与效率。
OREN: Octree Residual Network for Real-Time Euclidean Signed Distance Mapping
- 结合八叉树插值与神经网络残差，实现混合建模。
- 在保持高精度的同时，计算与内存开销接近体素方法。
- 适合需要实时性与连续可微性的机器人感知任务。
从点云数据重构符号距离函数（SDF）对机器人自主能力（如定位、建图、运动规划与控制）至关重要。现有在线大尺度SDF重建方法多依赖离散体素结构，影响SDF的连续性与可微性；而神经网络方法虽能实现高保真可微重构，但效率较低，易出现灾难性遗忘与内存限制，且常局限于截断SDF。本文提出OREN，一种融合八叉树显式先验与神经网络隐式残差的混合方法，实现了非截断（欧氏）SDF重构，其计算与内存效率与体素方法相当，同时具备神经网络的可微性与高精度。大量实验表明，OREN在准确率与效率上均优于现有最先进方法，为机器人与计算机视觉中的下游任务提供了可扩展解决方案。
原文摘要 · Abstract (English)
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.
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