A sparse representation-based approach is proposed to determine the salient views of 3D objects. The salient views are categorized into two groups. The first are boundary representative views that have several visible sides and object surfaces that may be attractive to humans. The second are side representative views that best represent views from sides of an approximating convex shape. The side representative views are class-specific and possess the most representative power compared to other within-class views. Using the concept of characteristic view class, we first present a sparse representation-based approach for estimating the boundary representative views. With the estimated boundaries, we determine the side representative views based on a minimum reconstruction error criterion. Furthermore, to evaluate our method, we introduce the notion of view-dependent dictionaries built from salient views for applications in 3D object recognition and retrieval. The proposed view-dependent dictionaries encode information on geometry across views and representation of the object. Through a series of experiments on four publicly available 3D object datasets, we demonstrate the effectiveness of our approach compared to two existing state-of-the-art algorithms and one baseline method.
Chen, Y. C., Patel, V. M., Chellappa, R., & Jonathon Phillips, P. (2015). Salient views and view-dependent dictionaries for object recognition. Pattern Recognition, 48(10), 3053–3066. https://doi.org/10.1016/j.patcog.2015.01.013