Object reconstruction is relevant for many autonomous robotic tasks that
require interaction with the environment. A key challenge in such scenarios is
planning view configurations to collect informative measurements for
reconstructing an initially unknown object. One-shot view planning enables
efficient data collection by predicting view configurations and planning the
globally shortest path connecting all views at once. However, geometric priors
about the object are required to conduct one-shot view planning. In this work,
we propose a novel one-shot view planning approach that utilizes the powerful
3D generation capabilities of diffusion models as priors. By incorporating such
geometric priors into our pipeline, we achieve effective one-shot view planning
starting with only a single RGB image of the object to be reconstructed. Our
planning experiments in simulation and real-world setups indicate that our
approach balances well between object reconstruction quality and movement cost.

通过利用扩散模型的强大的 3D 生成能力作为先验，我们提出了一种新颖的一次性视图规划方法，从而实现了仅通过对象的单个 RGB 图像开始进行高效的一次性视图规划，以平衡对象重建质量和移动成本。