Shape Completion with Prediction of Uncertain Regions

TL;DR

This paper introduces two novel methods for predicting uncertain regions in shape completion tasks, improving the accuracy of object reconstruction and grasp planning in robotics, especially under ambiguous views.

Contribution

The paper proposes two new approaches for predicting uncertain regions in shape completion, extending existing occupancy prediction methods, and provides a new dataset for training and evaluation.

Findings

Direct uncertainty prediction yields the highest accuracy in uncertain region segmentation.

Both proposed methods outperform existing baselines in shape completion and uncertainty prediction.

Avoiding uncertain regions enhances grasp quality across methods.

Abstract

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.