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.

翻译：物体重建对许多需要与环境交互的自主机器人任务至关重要。此类场景中的关键挑战在于规划视角配置以采集信息量丰富的测量值，从而重建初始未知的物体。单次视角规划通过一次性预测视角配置并规划连接所有视角的全局最短路径，实现了高效的数据采集。然而，执行单次视角规划需要关于物体的几何先验。在本工作中，我们提出了一种新颖的单次视角规划方法，利用扩散模型强大的三维生成能力作为先验。通过将此类几何先验纳入我们的流程，我们仅从待重建物体的单张RGB图像出发，即可实现有效的单次视角规划。在仿真与真实环境中的规划实验表明，我们的方法在物体重建质量与运动成本之间取得了良好平衡。