Landscape Perspective Distance-Included Angle Shape Distribution Analysis Based on 3D CAD Model Retrieval Algorithm

In view of the results obtained in the retrieval process of the 3D CAD model, which can show the differences in the local feature details of the model, the 3D CAD model retrieval algorithm is introduced into the analysis of the perspective distance-angle shape distribution of the garden landscape in this paper. Random sampling is performed on the surface of the constructed 3D CAD model, combined with the test distance between the sampling point and the neighboring points, and the corresponding garden landscape perspective distance-angle shape distribution characteristics in this area are calculated in order to achieve the similarity of the CAD model high-speed retrieval. Finally, experimental research shows that the algorithm proposed in this paper is better than the overall shape distribution algorithm and the spherical harmonic algorithm in the search performance of the CAD model, and it can effectively improve the recognition ability of the local detailed features of the 3D CAD model.

A Novel 3D CAD Model Retrieval Method Based on Vertices Classification and Weights Combination Optimization

3D shape retrieval is a problem of current interest in different fields, especially in the mechanical engineering domain. According to our knowledge, multifeature based techniques achieve the best performance at present. However, the practicability of those methods is badly limited due to the high computational cost. To improve the retrieval efficiency of 3D CAD model, we propose a novel 3D CAD model retrieval algorithm called VSC_WCO which consists of a new 3D shape descriptor named VSC and Weights Combination Optimization scheme WCO. VSC represents a 3D model with three distance distribution histograms based on vertices classification. The weighted sum of L1 norm distances between corresponding distance histograms of two VSC descriptors is regarded as dissimilarity of two models. For higher retrieval accuracy on a classified 3D model database, WCO is proposed based on Particle Swarm Optimization and existing class information. Experimental results on ESB, PSB, and NTU databases show that the discriminative power of VSC is already comparable to or better than several typical shape descriptors. After WCO is employed, the performance of VSC_WCO is similar to the leading methods by all performance metrics and is much better by computational efficiency.