Multispectral Domain Invariant Image for Retrieval-based Place Recognition

Multispectral recognition has attracted increasing attention from the research community due to its potential competence for many applications from day to night. However, due to the domain shift between RGB and thermal image, it has still many challenges to apply and to use RGB domain-based tasks. To reduce the domain gap, we propose multispectral domain invariant framework, which leverages the unpaired image translation method to generate a semantic and strongly discriminative invariant image by enforcing novel constraints in the objective function. We demonstrate the efficacy of the proposed method on mainly multispectral place recognition task and achieve significant improvement compared to previous works. Furthermore, we test on multispectral semantic segmentation and unsupervised domain adaptations to prove the scalability and generality of the proposed method. We will open our source code and dataset.