Reconstructed building models using stereo-based methods inevitably suffer from noise, leading to the lack of regularity which is characterized by straightness of structural linear features and smoothness of homogeneous regions. We leverage the structural linear features embedded in the mesh to construct a novel surface scaffold structure for model regularization. The regularization comprises two iterative stages: (1) the linear features are semi-automatically proposed from images by exploiting photometric and geometric clues jointly; (2) the scaffold topology represented by spatial relations among the linear features is optimized according to data fidelity and topological rules, then the mesh is refined by adjusting itself to the consolidated scaffold. Our method has two advantages. First, the proposed scaffold representation is able to concisely describe semantic building structures. Second, the scaffold structure is embedded in the mesh, which can preserve the mesh connectivity and avoid stitching or intersecting surfaces in challenging cases. We demonstrate that our method can enhance structural characteristics and suppress irregularities in the building models robustly in some challenging datasets. Moreover, the regularization can significantly improve the results of general applications such as simplification and non-photorealistic rendering.

In this paper, we study the problem of recognizing man-made objects and present a novel affine-invariant feature, Low-rank SIFT, which exploits the regular appearance property in man-made objects. The proposed feature achieves full affine invariance without needing to simulate over affine parameter space. We rectify local patches by converting them to their low-rank forms to achieve skew invariance, and perform the way similar to conventional SIFT to resolve rotation, translation and scaling ambiguity. The main contributions lie in two-fold: our method seeks to leverage low-rank prior to estimate affine parameters for local patches directly and we propose a fast algorithm to compute such parameters by introducing the Low-rank Integral Map. Besides, we describe a pipeline of constructing a geotagged building database from the ground up. We demonstrate the effectiveness of our approach in the application to place recognition.

In this paper, we propose a real-time object tracking approach. It utilizes generalized part-based appearance model and structure-constrained motion model as auxiliary. The appearance of the target object is modeled by the proposed generalized part-based appearance model, which combines the appearance of different parts of the target object, adaptively updated by an efficient structure learning scheme based on the online Passive-Aggressive algorithm. By integrating the confidence scores of multiple parts, mutual compensation is realized, significantly enhances the robustness of our method against the structure deformation and partial occlusion during the tracking. In addition, we enhance the performance of our tracker by using a motion model. It employs a structure-constrained rule, that is, the change on the structure of the target object between consecutive frames is small. Experiments on public video sequences verify the superior performance of our algorithm.