让3D场景图实时更新，自动识别并定位物体
REACT: Real-time Efficient Attribute Clustering and Transfer for Updatable 3D Scene Graph
- 用三元组损失训练嵌入模型，比较物体实例
- 实测可在保持效率下完成物体节点重定位
- 适合需要动态更新地图的机器人系统
现代自主机器人需要高层次地图表示以执行复杂任务。近年来，3D场景图（3DSGs）作为传统栅格地图的替代方案崭露头角，兼具高效内存使用和丰富的特征表达能力。然而，现有方法大多局限于静态环境。本文提出REACT框架，实现3DSG中物体节点的实时属性聚类与迁移重定位。该框架采用新型嵌入模型，基于三元组损失训练，实现物体实例间的有效比较，从而支持实例聚类与匹配。实验表明，REACT能在保证计算效率的前提下成功重定位物体。项目源码将开源，推动可复用、可更新3DSG的发展。
原文摘要 · Abstract (English)
Modern-day autonomous robots need high-level map representations to perform sophisticated tasks. Recently, 3D scene graphs (3DSGs) have emerged as a promising alternative to traditional grid maps, blending efficient memory use and rich feature representation. However, most efforts to apply them have been limited to static worlds. This work introduces REACT, a framework that efficiently performs real-time attribute clustering and transfer to relocalize object nodes in a 3DSG. REACT employs a novel method for comparing object instances using an embedding model trained on triplet loss, facilitating instance clustering and matching. Experimental results demonstrate that REACT is able to relocalize objects while maintaining computational efficiency. The REACT framework's source code will be available as an open-source project, promoting further advancements in reusable and updatable 3DSGs.
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