Efficient moving point handling for incremental 3D manifold reconstruction

TL;DR

This paper introduces an efficient method for handling moving points in incremental 3D manifold reconstruction, improving the management of dynamic points during scene modeling from sparse point clouds.

Contribution

We propose a novel policy for managing moving points in 3D Delaunay triangulation, reducing computational overhead in incremental scene reconstruction.

Findings

Our approach outperforms existing methods on KITTI sequences.

Significant reduction in processing time for point updates.

Maintains high reconstruction accuracy with dynamic points.

Abstract

As incremental Structure from Motion algorithms become effective, a good sparse point cloud representing the map of the scene becomes available frame-by-frame. From the 3D Delaunay triangulation of these points, state-of-the-art algorithms build a manifold rough model of the scene. These algorithms integrate incrementally new points to the 3D reconstruction only if their position estimate does not change. Indeed, whenever a point moves in a 3D Delaunay triangulation, for instance because its estimation gets refined, a set of tetrahedra have to be removed and replaced with new ones to maintain the Delaunay property; the management of the manifold reconstruction becomes thus complex and it entails a potentially big overhead. In this paper we investigate different approaches and we propose an efficient policy to deal with moving points in the manifold estimation process. We tested our approach with four sequences of the KITTI dataset and we show the effectiveness of our proposal in comparison with state-of-the-art approaches.