Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds
This work addresses the problem of symmetry estimation for tasks like shape completion and orientation estimation in 3D vision, but it is incremental as it builds on existing methods with a novel encoding approach.
The paper tackles the problem of estimating planar reflective symmetries from partial point clouds, presenting a method that uses a 2D convolutional recurrent regression scheme with a differentiable least squares step, achieving accuracy comparable to state-of-the-art techniques on full synthetic objects and improving outputs in a real-world 3D object detection pipeline.
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.