An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying
This addresses efficient 3D object retrieval for applications like computer vision and graphics, though it appears incremental with new descriptors and indexing.
The paper tackled 3D object retrieval by introducing QUICCI, a compact binary descriptor for local shape queries, and a Hamming tree indexing scheme with Weighted Hamming distance, achieving results on 828 million images from the SHREC2017 dataset.
A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment.