Robot grasp typically follows five stages: object detection, object
localisation, object pose estimation, grasp pose estimation, and grasp
planning. We focus on object pose estimation. Our approach relies on three
pieces of information: multiple views of the object, the camera's extrinsic
parameters at those viewpoints, and 3D CAD models of objects. The first step
involves a standard deep learning backbone (FCN ResNet) to estimate the object
label, semantic segmentation, and a coarse estimate of the object pose with
respect to the camera. Our novelty is using a refinement module that starts
from the coarse pose estimate and refines it by optimisation through
differentiable rendering. This is a purely vision-based approach that avoids
the need for other information such as point cloud or depth images. We evaluate
our object pose estimation approach on the ShapeNet dataset and show
improvements over the state of the art. We also show that the estimated object
pose results in 99.65% grasp accuracy with the ground truth grasp candidates on
the Object Clutter Indoor Dataset (OCID) Grasp dataset, as computed using
standard practice.

机器人抓取通常遵循五个阶段：物体检测、物体定位、物体姿态估计、抓取姿态估计和抓取规划。本论文关注于物体姿态估计，我们的方法依赖于物体的多个视角、相机在这些视角下的外部参数以及物体的 3D CAD 模型。我们通过优化不可区分渲染进行了粗略的姿态估计的细化，避免了点云或深度图像等其他信息的需求，并在 ShapeNet 数据集上验证了我们的物体姿态估计方法并显示了超过最先进方法的改进。通过使用标准实践在 Object Clutter Indoor Dataset (OCID) Grasp 数据集中计算出与地面真实抓取候选对象的 99.65％抓取准确度的物体姿态。