Approach For Sketch-based 3D Model Retrieval Research Articles

A novel sketch-based 3D model retrieval approach based on skeleton

<span>Since the skeleton represents the topology structure of the query sketch and 2D views of 3D model, this paper proposes a novel sketch-based 3D model retrieval algorithm which utilizes skeleton characteristics as the features to describe the object shape. Firstly, we propose advanced skeleton strength map (ASSM) algorithm to create the skeleton which computes the skeleton strength map by isotropic diffusion on the gradient vector field, selects critical points from the skeleton strength map and connects them by Kruskal's algorithm. Then, we propose histogram feature comparison algorithm which adopts the radii of the disks at skeleton points and the lengths of skeleton branches to extract the histogram feature, and compare the similarity between two skeletons using the histogram feature matrix of skeleton endpoints. Experiment results demonstrate that our approach which combines these two algorithms significantly outperforms several leading sketch-based retrieval approaches.</span>

Sketch-Based 3D Model Retrieval Using Attributes

With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.

A novel sketch-based 3D model retrieval method by integrating skeleton graph and contour feature

With the rapid growth of 3D models on the Web, effective methods to retrieve appropriate 3D models are becoming crucial. In this paper, we propose a novel sketch-based 3D model retrieval approach which integrates the skeleton graph and contour feature. Skeleton graph represents the topology structure of the query sketch as well as representative 2D views of 3D models, and contour feature captures their detailed information. Moreover, A hierarchical coarse-to-fine searching strategy is employed in this method. Firstly, a skeleton graph based matching method is used to eliminate those 3D models which are dissimilar to the query sketch. Then, the selected candidate models are refined by measuring the similarity of their contour feature. In addition, we adopt a graph transduction method to improve the ranking order of the retrieved 3D models. The proposed method was tested on the public sketch benchmarks and compared with other leading sketch-based 3D model retrieval methods. The experiment results demonstrate that our approach can accurately retrieve desired 3D models for users and has a superior performance to other state-of-the-art approaches.

A new sketch-based 3D model retrieval approach by using global and local features

With the rapid growth of available 3D models, fast retrieval of suitable 3D models has become a crucial task for industrial applications. This paper proposes a novel sketch-based 3D model retrieval approach which utilizes both global feature-based and local feature-based techniques. Unlike current approaches which use either global or local features, as well as do not take into account semantic relations between local features, we extract these two kinds of feature information from the representative 2D views of 3D models that can facilitate semantic description and retrieval for 3D models. Global features represent the gross exterior boundary shape information, and local features describe the interior details by compact visual words. Specifically, an improved bag-of-features method is provided to extract local features and their latent semantic relations. In addition, an efficient two-stage matching strategy is used to measure the distance between the query sketch and 3D models for selection and refinement. Experiment results demonstrate that our approach which combines these two kinds of complementary features significantly outperforms several state-of-the-art approaches.