  Structure-from-Motion (SfM) aims to recover 3D scene structures and camera
poses based on the correspondences between input images, and thus the ambiguity
caused by duplicate structures (i.e., different structures with strong visual
resemblance) always results in incorrect camera poses and 3D structures. To
deal with the ambiguity, most existing studies resort to additional constraint
information or implicit inference by analyzing two-view geometries or feature
points. In this paper, we propose to exploit high-level information in the
scene, i.e., the spatial contextual information of local regions, to guide the
reconstruction. Specifically, a novel structure is proposed, namely,
{\textit{track-community}}, in which each community consists of a group of
tracks and represents a local segment in the scene. A community detection
algorithm is used to partition the scene into several segments. Then, the
potential ambiguous segments are detected by analyzing the neighborhood of
tracks and corrected by checking the pose consistency. Finally, we perform
partial reconstruction on each segment and align them with a novel
bidirectional consistency cost function which considers both 3D-3D
correspondences and pairwise relative camera poses. Experimental results
demonstrate that our approach can robustly alleviate reconstruction failure
resulting from visually indistinguishable structures and accurately merge the
partial reconstructions.
