论文标题
TC-SFM：稳健的基于轨道社区的结构从胶片上移动
TC-SfM: Robust Track-Community-Based Structure-from-Motion
论文作者
论文摘要
从输入图像之间的对应关系以及由重复结构引起的歧义（即具有强视觉相似之处的不同结构）引起的歧义始终导致摄像头姿势和3D结构引起的歧义，旨在恢复3D场景结构和相机的结构，旨在恢复3D场景结构和姿势。为了处理歧义，大多数现有研究通过分析两种观察几何或特征点来求助于其他约束信息或隐式推理。在本文中，我们建议利用场景中的高级信息，即当地区域的空间上下文信息，以指导重建。具体来说，提出了一种新颖的结构，即{\ textit {track-community}}，其中每个社区由一组轨道组成，代表场景中的本地段。社区检测算法用于将场景分为几个部分。然后，通过分析轨道邻域并通过检查姿势一致性来检测潜在的模棱两可的段。最后，我们对每个段进行部分重建，并与新型的双向一致性成本函数对齐，该函数考虑了3D-3D对应关系和成对的相对摄像头姿势。实验结果表明，我们的方法可以牢固地减轻视觉上无法区分的结构而导致的重建失败，并准确合并部分重建。
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.
