MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

• URL: http://arxiv.org/abs/2412.09199v2
• Date: Fri, 13 Dec 2024 16:44:42 GMT
• Title: MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition
• Authors: Qiwen Gu, Xufei Wang, Fenglin Zhang, Junqiao Zhao, Siyue Tao, Chen Ye, Tiantian Feng, Changjun Jiang,
• Abstract summary: We introduce the mutual learning of viewpoint self-classification and Visual Place Recognition.<n>The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition.<n>Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels.
• Score: 13.681827205077727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance.To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels.

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