  Accurately estimating the shape of objects in dense clutters makes important
contribution to robotic packing, because the optimal object arrangement
requires the robot planner to acquire shape information of all existed objects.
However, the objects for packing are usually piled in dense clutters with
severe occlusion, and the object shape varies significantly across different
instances for the same category. They respectively cause large object
segmentation errors and inaccurate shape recovery on unseen instances, which
both degrade the performance of shape estimation during deployment. In this
paper, we propose a category-level shape estimation method for densely
cluttered objects. Our framework partitions each object in the clutter via the
multi-view visual information fusion to achieve high segmentation accuracy, and
the instance shape is recovered by deforming the category templates with
diverse geometric transformations to obtain strengthened generalization
ability. Specifically, we first collect the multi-view RGB-D images of the
object clutters for point cloud reconstruction. Then we fuse the feature maps
representing the visual information of multi-view RGB images and the pixel
affinity learned from the clutter point cloud, where the acquired instance
segmentation masks of multi-view RGB images are projected to partition the
clutter point cloud. Finally, the instance geometry information is obtained
from the partially observed instance point cloud and the corresponding category
template, and the deformation parameters regarding the template are predicted
for shape estimation. Experiments in the simulated environment and real world
show that our method achieves high shape estimation accuracy for densely
cluttered everyday objects with various shapes.
