Sketch-Based 3D Model Retrieval by Viewpoint Entropy-Based Adaptive View Clustering

Searching for relevant 3D models based on hand-drawn sketches is both intuitive and important for many applications, such as sketch-based 3D modeling and recognition. We propose a sketch-based 3D model retrieval algorithm by utilizing viewpoint entropy-based adaptive view clustering and shape context matching. Different models have different visual complexities, thus there is no need to keep the same number of representative views for each model. Motivated by this, we propose to measure the visual complexity of a 3D model by utilizing viewpoint entropy distribution of a set of sample views and based on the complexity value, we can adaptively decide the number of representative views. Finally, we perform Fuzzy C-Means based view clustering on the sample views based on their viewpoint entropy values. We test our algorithm on two latest sketch-based 3D model retrieval benchmarks and compare it with other four state-of-the-art approaches. The results demonstrate the superior performance and advantages of our algorithm.