A Similarity-Based Aspect-Graph Approach to 3D Object Recognition

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Multi-view Convolutional Neural Networks for 3D Shape Recognition (2015, background): “Cyr and Kimia [8] defined similarity metrics based on curve matching and grouped similar views, called aspect graphs of 3D models [18].”
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views (2015, methodology): “3D Object Detection Most 3D object detection methods are based on representing objects with discriminative features for points [4], patches [6] and parts [19, 29, 34], or by exploring topological structures [15, 2, 3].”
Discriminative Mixture-of-Templates for Viewpoint Classification (2010, background): “silhouette matching [10] or implicit shape models [9] or virtual-training [13]).”
3D generic object categorization, localization and pose estimation (2007, background): “The theory behind aspect graphs (AG) [14, 2, 4] has also offered a powerful and compact representation for 3D objects.”
Convolutional Models for Joint Object Categorization and Pose Estimation (2015, background): “Successful work have been done in estimating the object pose of a single object (Cyr & Kimia, 2004; Mei et al., 2011; Schels et al., 2012).”
Estimating image depth using shape collections (2014, methodology): “Most existing image-shape matching approaches [Cyr and Kimia 2004; Xu et al. 2011; Wang et al. 2013] convert the problem into an image matching problem, i.e., matching images with projected views of 3D shapes.”