Bingham Procrustean Alignment for Object Detection in Clutter
This addresses object detection in cluttered environments, but appears incremental as it builds on existing alignment methods.
The paper tackles object detection in cluttered RGB-D images by introducing Bingham Procrustean Alignment (BPA) to align models with the scene, achieving more reliable detections.
A new system for object detection in cluttered RGB-D images is presented. Our main contribution is a new method called Bingham Procrustean Alignment (BPA) to align models with the scene. BPA uses point correspondences between oriented features to derive a probability distribution over possible model poses. The orientation component of this distribution, conditioned on the position, is shown to be a Bingham distribution. This result also applies to the classic problem of least-squares alignment of point sets, when point features are orientation-less, and gives a principled, probabilistic way to measure pose uncertainty in the rigid alignment problem. Our detection system leverages BPA to achieve more reliable object detections in clutter.