  Many man-made objects are characterised by a shape that is symmetric along
one or more planar directions. Estimating the location and orientation of such
symmetry planes can aid many tasks such as estimating the overall orientation
of an object of interest or performing shape completion, where a partial scan
of an object is reflected across the estimated symmetry plane in order to
obtain a more detailed shape. Many methods processing 3D data rely on expensive
3D convolutions. In this paper we present an alternative novel encoding that
instead slices the data along the height dimension and passes it sequentially
to a 2D convolutional recurrent regression scheme. The method also comprises a
differentiable least squares step, allowing for end-to-end accurate and fast
processing of both full and partial scans of symmetric objects. We use this
approach to efficiently handle 3D inputs to design a method to estimate planar
reflective symmetries. We show that our approach has an accuracy comparable to
state-of-the-art techniques on the task of planar reflective symmetry
estimation on full synthetic objects. Additionally, we show that it can be
deployed on partial scans of objects in a real-world pipeline to improve the
outputs of a 3D object detector.
