Actively planning sensor views during object reconstruction is crucial for autonomous mobile robots. An effective method should be able to strike a balance between accuracy and efficiency. In this paper, we propose a seamless integration of the emerging implicit representation with the active reconstruction task. We build an implicit occupancy field as our geometry proxy. While training, the prior object bounding box is utilized as auxiliary information to generate clean and detailed reconstructions. To evaluate view uncertainty, we employ a sampling-based approach that directly extracts entropy from the reconstructed occupancy probability field as our measure of view information gain. This eliminates the need for additional uncertainty maps or learning. Unlike previous methods that compare view uncertainty within a finite set of candidates, we aim to find the next-best-view (NBV) on a continuous manifold. Leveraging the differentiability of the implicit representation, the NBV can be optimized directly by maximizing the view uncertainty using gradient descent. It significantly enhances the method's adaptability to different scenarios. Simulation and real-world experiments demonstrate that our approach effectively improves reconstruction accuracy and efficiency of view planning in active reconstruction tasks. The proposed system will 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于自主移动机器人至关重要。有效的方法需在精度与效率之间取得平衡。本文提出一种将新兴隐式表征与主动重建任务无缝融合的方法。我们构建隐式占据场作为几何代理，在训练过程中利用先验物体边界框作为辅助信息，生成干净且细节丰富的重建结果。为评估视角不确定性，我们采用基于采样的方法，直接从重建的占据概率场中提取熵作为视角信息增益的度量，从而避免了额外的不确定性图或学习需求。与先前在有限候选集中比较视角不确定性的方法不同，我们致力于在连续流形上寻找下一最佳视角（NBV）。利用隐式表征的可微性，可通过梯度下降直接最大化视角不确定性来优化NBV，显著增强了方法对不同场景的适应性。仿真与真实世界实验表明，本方法有效提升了主动重建任务中视角规划的精度与效率。所提系统将在 https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git 开源。