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.