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.

翻译：形状补全，即从部分观测中预测物体的完整几何结构，对于多项下游任务（尤其是机器人操作）具有高度相关性。当基于物体形状重建进行实际抓取规划或预测时，几何高度不确定性的指示是不可或缺的。特别是在给定具有歧义的物体视角时，关于整个物体部件是否存在的扩展区域可能存在不可约简的不确定性。为处理这一重要情况，我们提出了两种预测此类不确定区域的新方法，作为任何局部空间占据预测方法的直接扩展：一种通过对占据分数进行后处理，另一种通过直接预测不确定性指标。我们将这些方法与两种已知的概率形状补全方法进行比较。此外，我们基于ShapeNet生成了一个数据集，包含真实渲染的物体视角深度图像，并带有不确定区域的地面实况标注。我们在此数据集上进行训练，并在形状补全和不确定区域预测任务中测试每种方法，涵盖已知与新颖物体实例以及合成与真实数据。虽然直接不确定性预测在不确定区域分割方面准确性最高，但两种新方法在形状补全和不确定区域预测上均优于两个基线方法，且避免预测的不确定区域能提升所有测试方法的抓取质量。