C-DOG: Multi-View Multi-instance Feature Association Using Connected {\delta}-Overlap Graphs

TL;DR

This paper introduces C-DOG, a geometrical feature association algorithm for multi-view 3D reconstruction that robustly handles noisy detections and scene complexities without relying on appearance features.

Contribution

C-DOG employs a novel connected delta-overlap graph and robust clustering techniques to improve feature association accuracy in challenging multi-view scenarios.

Findings

Outperforms baseline geometry-based algorithms in synthetic benchmarks.

Remains effective with high object density and limited camera overlap.

Robust to noisy feature detections and scenes with no visual features.

Abstract

Multi-view multi-instance feature association constitutes a crucial step in 3D reconstruction, facilitating the consistent grouping of object instances across various camera perspectives. The presence of multiple identical objects within a scene often leads to ambiguities for appearance-based feature matching algorithms. Our work circumvents this challenge by exclusively employing geometrical constraints, specifically epipolar geometry, for feature association. We introduce C-DOG (Connected delta-Overlap Graph), an algorithm designed for robust geometrical feature association, even in the presence of noisy feature detections. In a C-DOG graph, two nodes representing 2D feature points from distinct views are connected by an edge if they correspond to the same 3D point. Each edge is weighted by its epipolar distance. Ideally, true associations yield a zero distance; however, noisy feature detections can result in non-zero values. To robustly retain edges where the epipolar distance is less than a threshold delta, we employ a Szymkiewicz--Simpson coefficient. This process leads to a delta-neighbor-overlap clustering of 2D nodes. Furthermore, unreliable nodes are pruned from these clusters using an Inter-quartile Range (IQR)-based criterion. Our extensive experiments on synthetic benchmarks demonstrate that C-DOG not only outperforms geometry-based baseline algorithms but also remains remarkably robust under demanding conditions. This includes scenes with high object density, no visual features, and restricted camera overlap, positioning C-DOG as an excellent solution for scalable 3D reconstruction in practical applications.