With the expansion of model scale and the improvement of model accuracy, the real-time rendering and displaying of 3D mesh models remain infeasible. To relieve such pressure, mesh simplification methods have been proposed to reduce the structural complexity while preserving the appearance. This work introduces a shape-preserving simplification method for urban building models. Compared to traditional simplification methods that only consider preserving local geometric features, we also consider the overall shapes of building models to avoid collapse with an increased simplification rate. The proposed method works in four steps. First, we filter mesh models to yield planar structures while preserving sharp features. Second, we detect the shapes of planar regions. Third, we simplify the planar and non-planar regions constrained by the overall shapes and local geometric features. Finally, we remap the texture. Experiments show the effectiveness of this method by evaluating it on various building models and comparing its performance with the original QEM algorithms. Furthermore, we maintain better spatial consistency in building models with different levels of detail (LOD) than traditional methods.

With the progress of photogrammetry and computer vision technology, three-dimensional (3D) reconstruction using aerial oblique images has been widely applied in urban modelling and smart city applications. However, state-of-the-art image-based automatic 3D reconstruction methods cannot effectively handle the unavoidable geometric deformation and incorrect texture mapping problems caused by moving cars in a city. This paper proposes a method to address this situation and prevent the influence of moving cars on 3D modelling by recognizing moving cars and combining the recognition results with a photogrammetric 3D modelling procedure. Through car detection using a deep learning method and multiview geometry constraints, we can analyse the state of a car’s movement and apply a proper preprocessing method to the geometrically model generation and texture mapping steps of 3D reconstruction pipelines. First, we apply the traditional Mask R-CNN object detection method to detect cars from oblique images. Then, a detected car and its corresponding image patch calculated by the geometry constraints in the other view images are used to identify the moving state of the car. Finally, the geometry and texture information corresponding to the moving car will be processed according to its moving state. Experiments on three different urban datasets demonstrate that the proposed method is effective in recognizing and removing moving cars and can repair the geometric deformation and error texture mapping problems caused by moving cars. In addition, the methods proposed in this paper can be applied to eliminate other moving objects in 3D modelling applications.

This study proposes a novel line-matching method that uses a disparity map in rectified image space for stereo aerial images. Two original images are first rectified to images in the rectified image space by a rectifying projection. The disparity map is then built by dense matching technology in the rectified image space. For an unmatched line segment, the surrounding pixels on the disparity map can be triangulated into 3D points, which can be used to compute rectified planar homography and obtain a predicted line segment. The candidate matches are determined by calculating line similarity as measured by position and orientation shifts with respect to the predicted line segment. Three pairs of calibrated aerial images were used to demonstrate the efficiency and validate the proposed method. Compared with another line-matching method using sparse image points, the number of matches, accuracy and completeness are improved in the proposed method.

In this paper, an automatic approach for zebra crossing extraction and reconstruction from high-resolution aerial images is proposed. In the extraction procedure, zebra crossings are extracted by the JointBoost classifier based on GLCM (Gray Level Co-occurrence Matrix) features and 2D Gabor Features. In the reconstruction procedure, a geometric parameter model based on spatial repeatability relationships is globally fitted to reconstruct the geometric shape of zebra crossings. Additionally, a group of representative experiments is conducted to test the proposed method under interfered conditions, such as zebra crossings covered by pedestrians, shadows and color fading. Furthermore, the performance of the proposed extraction method is compared with the template matching method. Finally, the results show the validation of our proposed method, both in the extraction and reconstruction of zebra crossings.