GPU-based Acceleration of Methods based on Clock Matching Metric for Large Scale 3D Shape Retrieval

Abstract

In this paper, we exploit the potential of the GPU in order to accelerate the process of 3D shape retrieval in large databases. Indeed, the massive parallelism of the GPU offers a huge performance in much high-performance computing (HPC) applications. Our solution consists to accelerate the shape matching process of methods that use a specific similarity metric called Clock Matching (CM). This CM measure is used by view-based methods as an efficient solution to compare two 3D models even if they are not presented in same pose and orientation by taking into account all possible poses in the matching phase. However, the increase in the number of comparisons has a strong influence on the execution time. Our challenge is to exploit the maximum benefit of GPU computing resource by considering the difficulty of implementing the CM metric on GPU. Indeed, the descriptor of a given 3D object is organized using a specific data structure (hash table), where only the information whose values are not equal to zero appears in the feature vector, which makes the parallelization on GPU to be not trivial. Experiment results show a reasonable benefit from the GPU approach.

Cloud-Based Image Retrieval Using GPU Platforms

“…In order to accelerate the matching process, several techniques based on distributed computing have been proposed in [18]. These solutions use CPUs only, while others implementations are implemented in parallel with GPU platforms [19,20]. Authors in [21] proposed a shape retrieval method using distributed databases that allowed to increase result precision.…”