3DOR 13

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/308

Browse

Browsing 3DOR 13 by Subject "H.3.3 [Computer Graphics]"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval
    (The Eurographics Association, 2013) Li, B.; Lu, Y.; Godil, A.; Schreck, Tobias; Aono, M.; Johan, H.; Saavedra, J. M.; Tashiro, S.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of sketch-based 3D shape retrieval methods based on a large scale hand-drawn sketch query dataset which has 7200 sketches and a generic 3D model target dataset containing 1258 3D models. The sketches and models are divided into 80 distinct classes. In this track, 5 runs have been submitted by 3 groups and their retrieval accuracies were evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, its corresponding evaluation code, and the comparative evaluation results will contribute to the progress of this research direction for the 3D model retrieval community.
  • Thumbnail Image
    Item
    Sketch-Based 3D Model Retrieval by Viewpoint Entropy-Based Adaptive View Clustering
    (The Eurographics Association, 2013) Li, Bo; Lu, Yijuan; Johan, Henry; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    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.