Interactive shape estimation for densely cluttered objects

Accurately recognizing the shape of objects in dense and cluttered scenes is important for robots to perform a variety of manipulation tasks, such as grasping and packing. However, the performance of previous shape estimation methods is not satisfactory due to the heavy occlusion between objects in dense clutter. In this paper, we propose an interactive exploration framework to estimate the shape of densely cluttered objects. Our framework utilizes pixel-wise uncertainty to generate efficient interactions, allowing to achieve a better trade-off between the shape estimation accuracy and the interaction cost. Specifically, the extracted features are utilized as network weights to predict the confidence of each proposal located on the surface of the objects. Proposals with higher confidence are considered reliable results for shape estimation. Meanwhile, we obtain the uncertainty of shape and scale estimation based on the confidence of each proposal, and further propose the adaptive fusion strategy to construct the pixel-wise estimation uncertainty height map. In addition, our proposed interaction strategy leverages the uncertainty height map to generate effective interaction actions to significantly improve the shape estimation accuracy for severely occluded objects. Therefore, the optimal accuracy-efficiency trade-off for shape estimation in dense clutter is achieved by iterating the shape estimation and interaction actions. Extensive experimental results verify the effectiveness of the proposed approach. Under challenging cases, the proposed approach has 66.7% and 52.0% less average Chamfer distance than direct estimation and random interaction, respectively.