利用人物交互动态分解重构场景，提升实时环境建模精度。
Proactive Scene Decomposition and Reconstruction
- 基于人-物交互行为，实时迭代分解与重建场景结构。
- 实现高精度相机与物体位姿估计，支持在线地图更新。
- 适合需要动态环境建模的AR/VR与机器人导航应用。
人类行为是场景动态的主要来源，其内在蕴含丰富的动态线索。本文提出一种主动场景分解与重建的新任务，采用在线方法，借助人-物交互行为，持续拆解并重构环境。通过观察这些有目的的交互，可动态优化分解与重建过程，解决静态物体级重建中的固有歧义。所提系统有效融合了动态环境中多个任务：精准的相机与物体位姿估计、实例分解及在线地图更新，充分利用第一人称直播流中的人-物交互线索，提供一种灵活、渐进的替代传统物体级重建的方法。借助高斯点阵技术，实现了保真度高且高效的动态场景建模与渲染。在多个真实场景中验证了其有效性，展现出显著优势。
原文摘要 · Abstract (English)
Human behaviors are the major causes of scene dynamics and inherently contain rich cues regarding the dynamics. This paper formalizes a new task of proactive scene decomposition and reconstruction, an online approach that leverages human-object interactions to iteratively disassemble and reconstruct the environment. By observing these intentional interactions, we can dynamically refine the decomposition and reconstruction process, addressing inherent ambiguities in static object-level reconstruction. The proposed system effectively integrates multiple tasks in dynamic environments such as accurate camera and object pose estimation, instance decomposition, and online map updating, capitalizing on cues from human-object interactions in egocentric live streams for a flexible, progressive alternative to conventional object-level reconstruction methods. Aided by the Gaussian splatting technique, accurate and consistent dynamic scene modeling is achieved with photorealistic and efficient rendering. The efficacy is validated in multiple real-world scenarios with promising advantages.
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