Plane Pair Matching for Efficient 3D View Registration

TL;DR

This paper introduces a lightweight, geometry-aware method for estimating motion between 3D views in indoor scenes by leveraging plane classifications under the Manhattan world assumption, improving accuracy with minimal computational cost.

Contribution

The paper proposes a novel plane pair matching approach that incorporates structural scene constraints to enhance 3D view registration accuracy and efficiency.

Findings

Improved registration precision over state-of-the-art methods.

Low computational overhead from planar constraints.

Effective separation of rotation and translation estimation.

Abstract

We present a novel method to estimate the motion matrix between overlapping pairs of 3D views in the context of indoor scenes. We use the Manhattan world assumption to introduce lightweight geometric constraints under the form of planes into the problem, which reduces complexity by taking into account the structure of the scene. In particular, we define a stochastic framework to categorize planes as vertical or horizontal and parallel or non-parallel. We leverage this classification to match pairs of planes in overlapping views with point-of-view agnostic structural metrics. We propose to split the motion computation using the classification and estimate separately the rotation and translation of the sensor, using a quadric minimizer. We validate our approach on a toy example and present quantitative experiments on a public RGB-D dataset, comparing against recent state-of-the-art methods. Our evaluation shows that planar constraints only add low computational overhead while improving results in precision when applied after a prior coarse estimate. We conclude by giving hints towards extensions and improvements of current results.