NDLI: Dense sampling and fast encoding for 3D model retrieval using bag-of-visual features

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Dense sampling and fast encoding for 3D model retrieval using bag-of-visual features

Content Provider	ACM Digital Library
Author	Furuya, Takahiko Ohbuchi, Ryutarou
Abstract	Our previous shape-based 3D model retrieval algorithm compares 3D shapes by using thousands of local visual features per model. A 3D model is rendered into a set of depth images, and from each image, local visual features are extracted by using the Scale Invariant Feature Transform (SIFT) algorithm by Lowe. To efficiently compare among large sets of local features, the algorithm employs bag-of-features approach to integrate the local features into a feature vector per model. The algorithm outperformed other methods for a dataset containing highly articulated yet geometrically simple 3D models. For a dataset containing diverse and detailed models, the method did only as well as other methods. This paper proposes an improved algorithm that performs equal or better than our previous method for both articulated and rigid but geometrically detailed models. The proposed algorithm extracts much larger number of local visual features by sampling each depth image densely and randomly. To contain computational cost, the method utilizes GPU for SIFT feature extraction and an efficient randomized decision tree for encoding SIFT features into visual words. Empirical evaluation showed that the proposed method is very fast, yet significantly outperforms our previous method for rigid and geometrically detailed models. For the simple yet articulated models, the performance was virtually unchanged.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781605584805
DOI	10.1145/1646396.1646430
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Approximate nearest neighbor Bag-of-features Gpu based algorithms Scale invariant feature transform Hashing Content-based retrieval Randomized decision tree Multi-scale feature
Content Type	Text
Resource Type	Article

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