  The choice of scene representation is crucial in both the shape inference
algorithms it requires and the smart applications it enables. We present
efficient and optimisable multi-class learned object descriptors together with
a novel probabilistic and differential rendering engine, for principled full
object shape inference from one or more RGB-D images. Our framework allows for
accurate and robust 3D object reconstruction which enables multiple
applications including robot grasping and placing, augmented reality, and the
first object-level SLAM system capable of optimising object poses and shapes
jointly with camera trajectory.
