# Bingham Procrustean Alignment for Object Detection in Clutter

Source: [Bingham Procrustean Alignment for Object Detection in Clutter](<https://www.sota2.com/research/paper/68fa60f21e209131ce9f1d83>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:49:34.015Z

Authors: Jared Glover, Sanja Popovic

Published: 2013-04-27T19:24:30.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1304.7399>)

[PDF](<https://arxiv.org/pdf/1304.7399.pdf>)