Shape completion, i.e., predicting the complete geometry of an object from a partial observation, is highly relevant for several downstream tasks, most notably robotic manipulation. When basing planning or prediction of real grasps on object shape reconstruction, an indication of severe geometric uncertainty is indispensable. In particular, there can be an irreducible uncertainty in extended regions about the presence of entire object parts when given ambiguous object views. To treat this important case, we propose two novel methods for predicting such uncertain regions as straightforward extensions of any method for predicting local spatial occupancy, one through postprocessing occupancy scores, the other through direct prediction of an uncertainty indicator. We compare these methods together with two known approaches to probabilistic shape completion. Moreover, we generate a dataset, derived from ShapeNet, of realistically rendered depth images of object views with ground-truth annotations for the uncertain regions. We train on this dataset and test each method in shape completion and prediction of uncertain regions for known and novel object instances and on synthetic and real data. While direct uncertainty prediction is by far the most accurate in the segmentation of uncertain regions, both novel methods outperform the two baselines in shape completion and uncertain region prediction, and avoiding the predicted uncertain regions increases the quality of grasps for all tested methods. Web: https://github.com/DLR-RM/shape-completion

翻译：形状补全，即从部分观测中预测物体的完整几何结构，与多项下游任务高度相关，尤其是机器人操作。当基于物体形状重建进行实际抓取的规划或预测时，对几何不确定性的指示不可或缺。特别是在给定模糊物体视图的情况下，关于完整物体部分是否存在的不确定性在扩展区域中可能是不可约的。为处理这一重要情况，我们提出两种新颖方法以预测此类不确定区域，这些方法可作为任何局部空间占据率预测方法的直接扩展：一种通过对占据率得分进行后处理，另一种通过直接预测不确定性指示器。我们将这些方法与两种已知的概率性形状补全方法进行了比较。此外，我们生成了一个基于ShapeNet的数据集，其中包含渲染逼真的深度图像，这些图像对应物体视图，并附有不确定区域的真值标注。我们在此数据集上训练，并测试了每种方法在已知与新型物体实例上的形状补全及不确定区域预测性能，同时评估了在合成数据与真实数据上的表现。在不确定区域分割方面，直接不确定性预测的准确性远高于其他方法，而这两种新方法在形状补全和不确定区域预测上均优于两种基线方法。避免预测的不确定区域可提高所有测试方法的抓取质量。网址：https://github.com/DLR-RM/shape-completion