Stereo-based category-level shape and 6D pose estimation methods have the potential to generalize to a wider range of materials than RGBD methods, which often suffer from depth measurement errors. However, without explicit depth from two views, parameters to be estimated can become inherently entangled, negatively impacting performance. To address this, we propose a method that leverages global stereo consistency to constrain optimization directions and mitigate parameter entanglement. We first estimate an intra-category occupancy field to represent a unified shape across views, ensuring consistency and preventing shape ambiguity. Through a divide-and-conquer approach within global shape fitting, we fit this shape to stereo images to obtain the pose, iteratively rendering normalized depth maps and exchanging information across views. This approach improves convergence toward the correct pose and scale. We validated our method on both depth-friendly and depth-challenging materials using our S-RGBD dataset and the TOD benchmark. Our method surpasses RGBD methods on challenging objects and performs comparably on depth-friendly ones. Ablation studies confirm the effectiveness of each component.

In this work, we tackle the problem of 7-DoF grasping pose estimation(6-DoF with the opening width of parallel-jaw gripper) from point cloud data, which is a fundamental task in robotic manipulation. Most existing methods adopt 3D voxel CNNs as the backbone for their efficiency in handling unordered point cloud data. However, we found that these approaches overlook detailed information of the point clouds, resulting in decreased performance. Through our analysis, we identified quantization loss and boundary information loss within 3D convolutional layers as the primary causes of this issue. To address these challenges, we introduced two novel branches: one adds an extra positional encoding operation to preserve details and unique features for each point, and the other uses a 2D CNN to operate on the range-based image, which better aggregates boundary information on a continuous 2D domain. To integrate these branches with the original branch, we introduced a novel multi-source fusion gated mechanism to aggregate features. Our approach achieved state-of-the-art performance on the Graspnet-1Billion benchmark and demonstrated high success rates in real robotic experiments across different scenes. Our work has the potential to improve the performance of robotic grasping systems and contribute to the field of robotics.