Scale-weighted dense bag of visual features for 3D model retrieval from a partial view 3D model

This paper describes a 3D shape model retrieval method that accepts, as a query, a 3D mesh obtained by a range scan from a viewpoint. The proposed method visually compares single depth map of the query with depth maps of a 3D model rendered from multiple viewpoints. Comparison of the depth maps employs bag-of local visual features extracted by using a modified version of Lowe's Scale-Invariant Feature Transform (SIFT). The method is capable of retrieving 3D models having diverse shape representations and is robust against articulation and global deformation of 3D shapes thanks to location-free integration of local visual features. Two modifications to the SIFT are made to avoid ill effects of range scanning artifacts, such as jagged edges and cracks, that exist in the query mesh. The two modifications are; (1) dense and random feature placement, and (2) importance sampling of low-frequency images in the SIFT's Gaussian image pyramid. Our experimental evaluation showed that the proposed method significantly outperforms previous methods.