Actively planning sensor views during object reconstruction is essential to autonomous mobile robots. This task is usually performed by evaluating information gain from an explicit uncertainty map. Existing algorithms compare options among a set of preset candidate views and select the next-best-view from them. In contrast to these, we take the emerging implicit representation as the object model and seamlessly combine it with the active reconstruction task. To fully integrate observation information into the model, we propose a supervision method specifically for object-level reconstruction that considers both valid and free space. Additionally, to directly evaluate view information from the implicit object model, we introduce a sample-based uncertainty evaluation method. It samples points on rays directly from the object model and uses variations of implicit function inferences as the uncertainty metrics, with no need for voxel traversal or an additional information map. Leveraging the differentiability of our metrics, it is possible to optimize the next-best-view by maximizing the uncertainty continuously. This does away with the traditionally-used candidate views setting, which may provide sub-optimal results. Experiments in simulations and real-world scenes show that our method effectively improves the reconstruction accuracy and the view-planning efficiency of active reconstruction tasks. The proposed system is going to open source at https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git.

翻译：在物体重建过程中主动规划传感器视角对自主移动机器人至关重要。该任务通常通过评估显式不确定度图的信息增益来实现。现有算法在预设候选视角集中比较选项并从中选取下一最优视角。与此不同，我们采用新兴的隐式表示作为物体模型，并将其与主动重建任务无缝结合。为将观测信息完整融入模型，我们提出一种专门针对物体级重建的监督方法，同时考虑有效空间和自由空间。此外，为直接从隐式物体模型评估视角信息，我们引入基于采样的不确定度评估方法。该方法直接从物体模型对射线上的点进行采样，将隐式函数推理的变异性作为不确定度度量，无需体素遍历或额外信息图。利用度量函数的可微性，可通过连续最大化不确定度优化下一最优视角，从而摒弃传统候选视角设置（该设置可能产生次优结果）。仿真与真实场景实验表明，本方法有效提升了主动重建任务的精度与视角规划效率。所提系统将开源于 https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git。