3 accepted papers
Local feature description is crucial for robotic tasks, yet existing methods struggle with motion blur, a prevalent challenge in high-dynamic and low-light environments. While effective on sharp images, they suffer significant degradation under blur. To address this issue, we propose Motion-Feat, an
Visual Place Recognition (VPR) techniques commonly utilize Contrastive Losses (CL) to train models that generate compact and discriminative global descriptors for images. These models often result in poor performance due to one of the following reasons during training: 1) loss functions that focus p…
Visual Place Recognition (VPR) utilizing patch descriptors from Convolutional Neural Networks (CNNs) has shown impressive performance in recent years. Existing works either perform exhaustive matching of all patch descriptors, or employ complex networks to select good candidate patches for further g…