Active object reconstruction using autonomous robots is gaining great interest. A primary goal in this task is to maximize the information of the object to be reconstructed, given limited on-board resources. Previous view planning methods exhibit inefficiency since they rely on an iterative paradigm based on explicit representations, consisting of (1) planning a path to the next-best view only; and (2) requiring a considerable number of less-gain views in terms of surface coverage. To address these limitations, we propose to integrate implicit representations into the One-Shot View Planning (OSVP). The key idea behind our approach is to use implicit representations to obtain the small missing surface areas instead of observing them with extra views. Therefore, we design a deep neural network, named OSVP, to directly predict a set of views given a dense point cloud refined from an initial sparse observation. To train our OSVP network, we generate supervision labels using dense point clouds refined by implicit representations and set covering optimization problems. Simulated experiments show that our method achieves sufficient reconstruction quality, outperforming several baselines under limited view and movement budgets. We further demonstrate the applicability of our approach in a real-world object reconstruction scenario.

翻译：利用自主机器人进行主动式物体重建正受到广泛关注。该任务的核心目标是在机载资源有限的条件下，最大化待重建物体的信息量。先前的视角规划方法存在效率低下的问题，因其依赖于基于显式表示的迭代范式，具体表现为：(1) 仅规划前往下一个最佳视角的路径；(2) 需要大量表面覆盖增益较低的视角。为解决这些局限性，我们提出将隐式表示集成到单次视角规划（OSVP）中。该方法的核心思路是利用隐式表示获取缺失的小面积表面区域，而非通过额外视角进行观测。为此，我们设计了一种名为OSVP的深度神经网络，能够根据从初始稀疏观测精化得到的稠密点云，直接预测一组视角。为训练OSVP网络，我们利用经隐式表示精化的稠密点云和集合覆盖优化问题生成监督标签。仿真实验表明，本方法在有限视角与移动预算条件下，能够达到足够的重建质量，且性能优于多种基线方法。我们进一步在真实物体重建场景中验证了所提方法的实用性。