影像二重者：學習消除相似結構的影像

摘要

我們考慮視覺消歧任務，即確定一對視覺上相似的圖像是否描繪相同或不同的3D表面（例如，對稱建築的同一側或相對側）。虛假圖像匹配指的是兩幅圖像觀察到不同但在視覺上相似的3D表面，這對人類來說可能難以區分，也可能導致3D重建算法生成錯誤結果。我們提出了一種基於學習的視覺消歧方法，將其制定為圖像對的二元分類任務。為此，我們引入了一個新的數據集Doppelgangers，用於解決這個問題，其中包含具有真實標籤的相似結構的圖像對。我們還設計了一種網絡架構，該架構將局部關鍵點和匹配的空間分佈作為輸入，從而更好地推理局部和全局線索。我們的評估顯示，我們的方法可以在困難情況下區分虛假匹配，並可以集成到SfM流程中以生成正確、消歧的3D重建。請查看我們的項目頁面以獲取代碼、數據集和更多結果：http://doppelgangers-3d.github.io/。

English

We consider the visual disambiguation task of determining whether a pair of visually similar images depict the same or distinct 3D surfaces (e.g., the same or opposite sides of a symmetric building). Illusory image matches, where two images observe distinct but visually similar 3D surfaces, can be challenging for humans to differentiate, and can also lead 3D reconstruction algorithms to produce erroneous results. We propose a learning-based approach to visual disambiguation, formulating it as a binary classification task on image pairs. To that end, we introduce a new dataset for this problem, Doppelgangers, which includes image pairs of similar structures with ground truth labels. We also design a network architecture that takes the spatial distribution of local keypoints and matches as input, allowing for better reasoning about both local and global cues. Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions. See our project page for our code, datasets, and more results: http://doppelgangers-3d.github.io/.

影像二重者：學習消除相似結構的影像

Doppelgangers: Learning to Disambiguate Images of Similar Structures

摘要

Support