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PGVS: A probabilistic graph-theoretic framework for view-graph selection in structure-from-motion
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DOI：10.1016/j.isprsjprs.2025.11.004.png)
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Abstract
En 中文
The underlying view-graph is generally constructed through the matching of unordered image paris, which establish a crucial step in ensuring the accuracy and efficiency of the structure-from-motion (SfM) process. However, the initial graph often contain redundant and erroneous edges, which arise from incorrect image retrieval and ambiguous structures(e.g., symmetric buildings with identical or opposing facets), leading to the emergence of ghosting effects and superimposed reconstruction artifacts. Most contemporary approaches employ bespoke solutions to attain specific reconstruction goals, such as efficiency, precision, or disambiguation. In contrast to these task-specific methods, we propose a probabilistic graph-theoretic framework, termed PGVS, which formulates the view-graph selection problem as a weighted maximum clique optimization problem, achieving both sparsification and disambiguation simultaneously. Furthermore, we develop a sophisticated binary penalty continuous relaxation technique to derive a solution that is guaranteed to correspond to the optimal outcome of the original problem. In contrast to techniques for verifying pose consistency, we introduce a context-aware graph similarity assessment mechanism that is based on view triplets with a multi-view patch tracking strategy. This approach helps alleviate the effects of vanishing keypoints and environmental occlusions and reduces the impact of erroneous image correspondences that often undermine the reliability of pose estimation. Moreover, we develop a Bayesian inference framework to evaluate edge-level consistency analysis over the context graph, enabling us to estimate the likelihood that each edge reflects a globally coherent match. This probabilistic characterization is then leveraged to construct the adjacency matrix for a weighted maximum clique formulation. To solve this combinatorial problem, we employ a continuous binary-penalty relaxation technique, which enables us to obtain an optimal solution reflecting global consistency with the highest matching affinity and confidence. The resulting selected view-graph constitutes a novel and efficient algorithmic component that can be seamlessly integrated as a preprocessing module into any SfM pipeline, thereby enhancing its adaptability and general applicability. We validate the efficacy of our method on both generic and ambiguous datasets, which cover a wide spectrum of small, medium, and large-scale datasets, each exhibiting distinct statistical characteristics. In generic datasets, our approach significantly reduces reconstruction time by removing redundant edges to sparsify the view-graph while preserving accuracy and mitigating ghosting artifacts. For ambiguous datasets, our method excels in identifying erroneous matches, even under highly challenging conditions, leading to accurate, disambiguated, and unsuperimposed 3D reconstructions. The source code of our approach is publicly available at https://github.com/zhoupengwei/PGVS .
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