Hash Indexing-Based Image Matching for 3D Reconstruction

Abstract

Image matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of modern image matching methods, this paper proposes an image matching method for 3D reconstruction. The proposed method can obtain high matching accuracy through hash index in a very short amount of time. The core of hash matching includes two parts: creating the hash table and hash index. The former is used to encode local feature descriptors into hash codes, and the latter is used to search candidates for query feature points. In addition, the proposed method is extremely robust to image scaling and transformation by using various verifications. A comprehensive experiment was carried out using several challenging datasets to evaluate the performance of hash matching. Experimental results show that the HashMatch presents excellent results compared to the state-of-the-art methods in both computational efficiency and matching accuracy.

Efficient Feature Matching for Large-scale Images based on Cascade Hash and Local Geometric Constraint

“…For example, the SIFTGPU algorithm proposed by Wu (2013) accelerates the traditional SIFT algorithm and stands as a mainstream algorithm in current feature matching. Cao et al (2023) proposed a feature matching approach based on hash indexing. Cheng et al (2014) proposed a Cascade Hashing approach to accelerate feature matching.…”

“…While fast, constructing KD-trees is relatively complex, and its performance with high-dimensional features is suboptimal (Silpa-Anan and Hartley, 2008). Hash-based matching rapidly finds matching features by mapping features to a hash table (Cheng et al, 2014;Cao et al, 2023). While efficient, hash collisions and sensitivity to noise can be issues.…”