🤖 AI Summary
This work addresses the failure of traditional Structure-from-Motion (SfM) methods in planar scenes due to degeneracy in epipolar geometry. The authors propose a unified framework that leverages homography graph embedding to actively exploit planar structures as geometric constraints. By decomposing multi-view homographies induced by dominant planes, relative camera poses are estimated and organized into a pose graph. Global optimization is then achieved through spectral embedding combined with a maximum spanning tree for consensus maximization. The method significantly outperforms existing SfM approaches in strongly planar environments—such as basketball courts—and achieves state-of-the-art or comparable performance on the IMC Phototourism outdoor benchmark, demonstrating robust camera pose recovery in both degenerate and non-degenerate scenarios.
📝 Abstract
Structure from Motion (SfM) systems traditionally struggle with planar scenes, where standard epipolar geometry-based methods become degenerate. Rather than viewing planar surfaces as a limitation, we propose a unified framework that leverages them as a source of geometric constraints. Our key insight is that each planar surface visible across multiple views provides an independent estimate of relative camera poses through homography decomposition. By aggregating estimates from multiple planes or even from a single dominant plane we achieve robust pose recovery in scenarios where traditional methods fail. We introduce a novel graph-based approach that constructs a pose-graph from homography estimates and employs spectral embedding to identify and filter unreliable edges. Our method maps homography-based pose estimates onto the real line based on their geometric and visual consistency, enabling efficient extraction of a maximally consistent spanning tree for pose recovery. This approach naturally handles both highly planar scenes, such as indoor sports arenas, and general $3$D environments. We demonstrate superior performance on basketball court imagery where existing methods struggle, while matching or exceeding state-of-the-art results on unconstrained outdoor scenes from the IMC Phototourism benchmark.