# An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying

arXiv: https://arxiv.org/abs/2008.02916 · HTML: https://mlsanity.com/paper/2008.02916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing](https://mlsanity.com/paper/2107.03368/) (2021)

## Abstract (arXiv)

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.