In this paper, we describe a shape-based 3D model retrieval method based on multi-scale local visual features. The features are extracted from 2D range images of the model viewed from uniformly sampled locations on a view sphere. The method is appearance-based, and accepts all the models that can be rendered as a range image. For each range image, a set of 2D multi-scale local visual features is computed by using the Scale Invariant Feature Transform [22] algorithm. To reduce cost of distance computation and feature storage, a set of local features describing a 3D model is integrated into a histogram using the Bag-OfFeatures approach. Our experiments using two standard benchmarks, one for articulated shapes and the other for rigid shapes, showed that the methods achieved the performance comparable or superior to some of the most powerful 3D shape retrieval methods.  KEYWORDS: Three-dimensional (3D) models have become ubiquitous, for games running on mobile-phones and on game consoles, for such Web-based applications as the Google Earth, for medical diagnostics, and for mechanical or architectural design. The need to organize these 3D models, for example for effective reuse, has prompted research into shape-based retrieval of 3D models [35, 15] . A 3D shape comparison method must satisfy several requirements for invariance. A typical set of requirements includes (1) invariance to similarity transformations, (2) invariance to shape representations, (3) invariance to geometrical and topological noise, and (4) invariance to articulation or global deformation. Most of the 3D model retrieval methods try to satisfy the invariance to a certain class of geometrical transformation. The invariance to geometrical transformation can be gained by using a shape feature inherently invariant to the class of transformation, by using pose normalization, of by a combination of both. Some shape comparison methods are very tolerant of shape representations, accepting polygonal meshes, polygon soup, or even point sets. Others, however, assume certain shape representation, e.g., watertight mesh. The invariance to noise and error in geometry and/or topology is important if similarity, not exact, matching is desired. This invariance to noise and error is also related to the invariance or tolerance to shape representation. The invariance to articulation, or pose change, of 3D models is addressed much less frequently than the other invariance mentioned above. Figure 1 shows examples of articulated 3D models. Using a typical shape comparison method, two models in the "human" class (or "snake" class) will not be very similar. "human" class "snake" class In this paper, we propose a method for shape-based 3D model retrieval that performs well for both articulated and rigid models. The method also has a high degree of invariance to shape representation; the method accepts a diverse set of 3D shape representation so far as range image can be rendered. The method describes a 3D model by using a set of local, multi-scale, salient, visual features. The method first renders asset of range images of the model from multiple view directions about the model, as in the Light Field Descriptor (LFD) [5] or the Multiple-Orientation Depth Fourier Descriptor (MODFD) [25] . To extract local features from each range image, the proposed method uses the Scale Invariant Feature Transform (SIFT) algorithm proposed by Lowe [Lowe04]. As each depth image yields a few dozen features, and there are a few dozen range images per model, a 3D model is associated with thousands of local features. Computing dissimilarity between two sets of local features having thousands of local features each can be quite expensive. Assuming n features per model, comparing all the pairs of features of two models would cost 2 ( ) O n . For a database of nontrivial size, such method would take too long to search. The cost of storing all the local features for large number of models per database is also very high. Our proposed method avoids the costly pair-wise distance computation by integrating all the local features of a model into a single feature vector by using the Bag-Of-Features (BoF) approach. The bag-of-features approach is inspired originally by the bag-of-words approach in text retrieval, which characterizes a text document by a histogram of words' occurrences in the document. In our proposed method, vectorquantized local features, or visual words, from multiple range images are accumulated into single histogram to become a feature vector for the 3D model. The codebook for the vector quantization is learned via k-means clustering of local features extracted from the 3D models in the database. The bag-of-features approach is simple to implement, efficient to run, and as the experiments show, quite effective in retrieving 3D models. † 4-3-11
Ohbuchi et al. REF extract features from 2D range images of the model viewed from uniformly sampled locations on a view sphere.
Salient local visual features for shape-based 3D model retrieval
2008 IEEE International Conference on Shape Modeling and Applications
2008 IEEE International Conference on Shape Modeling and Applications
Computer Science