MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery

• URL: http://arxiv.org/abs/2202.09146v1
• Date: Fri, 18 Feb 2022 11:53:01 GMT
• Title: MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery
• Authors: Ahmad Khaliq, Michael Milford and Sourav Garg
• Abstract summary: We augment NetVLAD representation learning with low-resolution image pyramid encoding. The resultant multi-resolution feature pyramid can be conveniently aggregated through VLAD into a single compact representation. We show that the underlying learnt feature tensor can be combined with existing multi-scale approaches to improve their baseline performance.
• Score: 28.875236694573815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Place Recognition (VPR) is a crucial component of 6-DoF localization, visual SLAM and structure-from-motion pipelines, tasked to generate an initial list of place match hypotheses by matching global place descriptors. However, commonly-used CNN-based methods either process multiple image resolutions after training or use a single resolution and limit multi-scale feature extraction to the last convolutional layer during training. In this paper, we augment NetVLAD representation learning with low-resolution image pyramid encoding which leads to richer place representations. The resultant multi-resolution feature pyramid can be conveniently aggregated through VLAD into a single compact representation, avoiding the need for concatenation or summation of multiple patches in recent multi-scale approaches. Furthermore, we show that the underlying learnt feature tensor can be combined with existing multi-scale approaches to improve their baseline performance. Evaluation on 15 viewpoint-varying and viewpoint-consistent benchmarking datasets confirm that the proposed MultiRes-NetVLAD leads to state-of-the-art Recall@N performance for global descriptor based retrieval, compared against 11 existing techniques. Source code is publicly available at https://github.com/Ahmedest61/MultiRes-NetVLAD.

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