论文标题
通过稳定性和连通性进行机器人操作的Amodal 3D重建
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
论文作者
论文摘要
基于学习的3D对象重建可以实现3D对象模型的单一或几次估算。对于机器人技术，这具有允许基于模型的方法快速适应新颖对象和场景的潜力。现有的3D重建技术优化了视觉重建保真度，通常通过倒角距离或体素iou测量。我们发现，当应用于现实，混乱的机器人环境时，这些系统会产生具有较低物理现实主义的重建，从而在用于基于模型的控制的情况下导致任务性能差。我们提出了ARM是一种Amodal 3D重建系统，它在对象形状上引入了（1）稳定性，（2）先验的连接性和（3）多通道输入表示表示，该表示允许对对象组之间的关系进行推理。通过对物体的物理特性使用这些先验，我们的系统不仅可以通过标准视觉指标来提高重建质量，而且还可以在挑战性，混乱的环境中对基于模型的机器人操纵任务进行基于模型的控制。代码可在github.com/wagnew3/arm上找到。
Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes. Existing 3D reconstruction techniques optimize for visual reconstruction fidelity, typically measured by chamfer distance or voxel IOU. We find that when applied to realistic, cluttered robotics environments, these systems produce reconstructions with low physical realism, resulting in poor task performance when used for model-based control. We propose ARM, an amodal 3D reconstruction system that introduces (1) a stability prior over object shapes, (2) a connectivity prior, and (3) a multi-channel input representation that allows for reasoning over relationships between groups of objects. By using these priors over the physical properties of objects, our system improves reconstruction quality not just by standard visual metrics, but also performance of model-based control on a variety of robotics manipulation tasks in challenging, cluttered environments. Code is available at github.com/wagnew3/ARM.
