Current view planning (VP) systems usually adopt an iterative pipeline with next-best-view (NBV) methods that can autonomously perform 3D reconstruction of unknown objects. However, they are slowed down by local path planning, which is improved by our previously proposed set-covering-based network SCVP using one-shot view planning and global path planning. In this work, we propose a combined pipeline that selects a few NBVs before activating the network to improve model completeness. However, this pipeline will result in more views than expected because the SCVP has not been trained from multiview scenarios. To reduce the overall number of views and paths required, we propose a multiview-activated architecture MA-SCVP and an efficient dataset sampling method for view planning based on a long-tail distribution. Ablation studies confirm the optimal network architecture, the sampling method and the number of samples, the NBV method and the number of NBVs in our combined pipeline. Comparative experiments support the claim that our system achieves faster and more complete reconstruction than state-of-the-art systems. For the reference of the community, we make the source codes public.

翻译：当前视角规划系统通常采用基于下一最佳视角的迭代流程，能够自主完成未知物体的三维重建。然而，该方法受限于局部路径规划导致速度较慢，我们先前提出的基于集合覆盖的网络SCVP通过一次性视角规划与全局路径规划改进了这一问题。本研究提出一种组合式流程，在激活网络前选取少量下一最佳视角以提升模型完整性。但由于SCVP未经过多视角场景训练，该流程会导致视角数量超出预期。为此，我们提出多视角激活架构MA-SCVP，并基于长尾分布设计了一种高效的视角规划数据集采样方法。消融实验验证了最优网络架构、采样方法及采样数量、组合流程中下一最佳视角方法及其选取数量。对比实验表明，本系统相比现有最优方法实现了更快速、更完整的重建。我们已将源代码开源供学术界参考。