Multidimensional similarity structure analysis
Multidimensional similarity structure analysis
The crust and the &Bgr;-Skeleton: combinatorial curve reconstruction
Graphical Models and Image Processing
A Similarity-Based Aspect-Graph Approach to 3D Object Recognition
International Journal of Computer Vision
Shape Complexity Based on Mutual Information
SMI '05 Proceedings of the International Conference on Shape Modeling and Applications 2005
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Towards Stable and Salient Multi-View Representation of 3D Shapes
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
Partial and approximate symmetry detection for 3D geometry
ACM SIGGRAPH 2006 Papers
Shape Measure for Identifying Perceptually Informative Parts of 3D Objects
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Finding optimal views for 3D face shape modeling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Sketch-based 3D model retrieval by viewpoint entropy-based adaptive view clustering
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
| Hi-index | 0.00 |
We present an approach to compute the perceived complexity of a given 3D shape using the similarity between its views. Previous studies on 3D shape complexity relied on geometric and/or topological properties of the shape and are not appropriate for incorporating results from human shape perception which claim that humans perceive 3D shapes as organizations of 2D views. Therefore, we base our approach to computing 3D shape complexity on the (dis)similarity matrix of the shape's 2D views. To illustrate the application of our approach, we note that simple shapes lead to similar views whereas complex ones result in different, dissimilar views. This reflected in the View Similarity Graph (VSG) of a shape as tight clusters of points if the shape is simple and increasingly dispersed points as it gets more complex. To get a visual intuition of the VSG, we project it to 2D using Multi-Dimensional Scaling (MDS) and introduce measures to compute shape complexity through point dispersion in the resulting MDS plot. Experiments show that results obtained using our measures alleviate some of the drawbacks present in previous approaches.