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.
翻译:精确地估计密布层中天体的形状对机器人包装有重要贡献, 因为最佳天体安排要求机器人规划器获取所有存在对象的形状信息。 但是, 包装对象通常会堆积在密集的块状中, 严重隔离, 而同一类别的对象形状在不同的场合中差异很大。 它们分别造成大型天体分解错误, 并且无法准确的形状恢复到不可见的事例中, 这些事例在部署时会降低形状估计的性能。 在本文中, 我们为密布密集的天体提出一个分类级形状估计方法。 我们的框架天体间隔通过多视图视觉视觉信息组合来获取所有已存在对象的形状的形状信息。 我们的框架天体形状将显示多视图 RGB 图像的视觉模板和从千分级点点云层中获取的星体显示高分解准确度, 而实例形状的形状则通过拆分解的分类来恢复。 具体来说, 我们首先收集天体的多视图 RGB- D 图像的多视图, 然后将显示从直观的图像的图像和从焦点点值中了解到的图像, 直观中, 直观的直观的直径的直观的直观的直径的云状图显示的云状状图显示的直径的图像是最终的直径的直方的直方的直径的直方的直径的云体的直方的直径方的云体 。