论文标题
映射：使用稀疏分层隐式神经表示大规模3D映射
SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
论文作者
论文摘要
大型环境的准确映射是大多数户外自动群系统的重要组成部分。传统映射方法的挑战包括记忆消耗和映射准确性之间的平衡。本文解决了使用由3D激光雷达测量构建的隐式表示实现大规模3D重建的问题。我们通过基于OCTREE的层次结构来学习和存储隐式功能，该结构稀疏且可扩展。隐式特征可以通过浅神经网络变成签名的距离值。我们利用二进制交叉熵损失以3D测量作为监督来优化本地特征。根据我们的隐式表示，我们设计了一个具有正则化的增量映射系统，以解决忘记持续学习的问题。我们的实验表明，与当前最新3D映射方法相比，我们的3D重建更准确，完整和记忆效率。
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems. Challenges of traditional mapping methods include the balance between memory consumption and mapping accuracy. This paper addresses the problem of achieving large-scale 3D reconstruction using implicit representations built from 3D LiDAR measurements. We learn and store implicit features through an octree-based, hierarchical structure, which is sparse and extensible. The implicit features can be turned into signed distance values through a shallow neural network. We leverage binary cross entropy loss to optimize the local features with the 3D measurements as supervision. Based on our implicit representation, we design an incremental mapping system with regularization to tackle the issue of forgetting in continual learning. Our experiments show that our 3D reconstructions are more accurate, complete, and memory-efficient than current state-of-the-art 3D mapping methods.
