LocaliseBot: Multi-view 3D object localisation with differentiable
  rendering for robot grasping
Sujal Vijayaraghavan Redwan Alqasemi Rajiv Dubey Sudeep Sarkar
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.