We propose MAP-NBV, a prediction-guided active algorithm for 3D reconstruction with multi-agent systems. Prediction-based approaches have shown great improvement in active perception tasks by learning the cues about structures in the environment from data. But these methods primarily focus on single-agent systems. We design a next-best-view approach that utilizes geometric measures over the predictions and jointly optimizes the information gain and control effort for efficient collaborative 3D reconstruction of the object. Our method achieves 22.75% improvement over the prediction-based single-agent approach and 15.63% improvement over the non-predictive multi-agent approach. We make our code publicly available through our project website: http://raaslab.org/projects/MAPNBV/

翻译：我们提出MAP-NBV，一种面向多智能体系统三维重建的预测引导主动算法。基于预测的方法通过从数据中学习环境结构线索，在主动感知任务中展现出显著提升，但现有方法主要集中于单智能体系统。我们设计了一种利用预测几何度量的下一最佳视角方法，通过联合优化信息增益与控制代价，实现高效的物体协同三维重建。该方法相较于基于预测的单智能体方法提升22.75%，较非预测多智能体方法提升15.63%。我们已在项目网站（http://raaslab.org/projects/MAPNBV/）公开代码。