Mesh saliency

By Chang Ha Lee, Amitabh Varshney, David W. Jacobs. ACM Transactions on Graphics, 2005

815 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dbbacafa8dcc2ff25b4f633cd91423038e9cb77f

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Depth saliency based on anisotropic center-surround difference (2014, methodology): “An exception is CURV, which is based on depth curvature.”
Shape2Pose (2014, methodology): “Previous methods have considered salience measures based on analysis of curvatures [Gal and Cohen-Or 2006; Lee et al. 2005], shape similarities [Shilane and Funkhouser 2007], and other properties [Chen et al.”
On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes (2009, background): “Some work on mesh saliency from human perception point of view also exists (Lee et al. 2005) however, we are interested in points that are significant from pattern recognition point of view and can be repeatably identified by computer vision algorithms.”
Schelling points on 3D surface meshes (2012, methodology): “For example, Figure 11 shows how a predicted Schelling distribution can guide a mesh simplification algorithm to preserve features with higher expected semantic salience (as in [Lee et al. 2005]).”
Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes (2011, background): “Some tasks that benefit from this capability are object registration [5], object retrieval and matching [9], mesh simplification, viewpoint selection [14], and mesh segmentation [11, 25], just to name a few.”
Attentive Systems: A Survey (2017, background): “Lee et al. (2005) introduce the idea of mesh saliency as a measure of regional importance for graphics meshes.”
What’s in an image? (2005, background): “[17] defines the most informative view as that which maximizes the visible saliency of an object.”
A planar-reflective symmetry transform for 3D shapes (2006, methodology): “With this relatively simple scoring function it is enough to do an exhaustive search to find the optimal viewpoint, although a gradient decent method such as introduced by [Lee et al. 2005] may be used to accelerate the computation.”