# Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing

arXiv: https://arxiv.org/abs/2107.03368 · HTML: https://mlsanity.com/paper/2107.03368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 54 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure](https://mlsanity.com/paper/1912.05848/) (2019)
- [An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying](https://mlsanity.com/paper/2008.02916/) (2020)

## Abstract (arXiv)

A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.