Abstract
In this paper, we propose a 3 dimensional (3D) model identification method based on weighted implicit shape representation (WISR) and panoramic view. The WISR is used for 3D shape normalization. The 3D shape normalization method normalizes a 3D model by scaling, translation, and rotation with respect to the scale factor, center, and principal axes. The major advantage of the WISR is reduction of the influences caused by shape deformation and partial removal. The well-known scale-invariant feature transform descriptors are extracted from the panoramic view of the 3D model for feature matching. The panoramic view is a range image obtained by projecting a 3D model to the surface of a cylinder which is parallel to a principal axis determined by the 3D shape normalization. Because of using only one range image, the proposed method can provide small size of features and fast matching speed. The precision of the identification is 92% with 1200 models that consist of 24 deformed versions of 50 classes. The average feature size and matching time are 4.1 KB and 1.9 s.
Highlights
Development of 3 dimensional (3D) printing technology has led to the explosive growth of 3D models recently
A 2 dimensional (2D) view image is a range image obtained from a viewpoint located on a 3D model’s bounding sphere
It is obtained by projecting a 3D model onto the surface of a cylinder, which is parallel to a principal axis determined by 3D shape normalization
Summary
Development of 3 dimensional (3D) printing technology has led to the explosive growth of 3D models recently. The existent approaches suffer from big size of features and slow matching speed To overcome these problems, we propose an approach using only one range image, which means a panoramic view is used for identification. The panoramic view bridges the gaps between the range images rendered from multiple views It is obtained by projecting a 3D model onto the surface of a cylinder, which is parallel to a principal axis determined by 3D shape normalization. If the shape normalization cannot determine the principal axes of a query model as similar as those of original model in database, the identification needs many more range images to match them. The 3D shape normalization uses a weighted ISR (WISR) to reduce the influence caused by shape deformation and partial removal It estimates the number of clusters based on rate distortion theory [15]. In the section of experimental results, we show the comparisons between the precision of identification of the proposed method and those of other methods
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