Clustering-Based Psychometric No-Reference Quality Model For Point Cloud Video

Image based place recognition has achieved state of the art performance on terrestrial image datasets, however there is very little publicly available research on how these systems perform on waterborne imagery in order to carry out place recognition for autonomous sea vessels. This domain may provide new visual challenges such as water obstruction, distance from shore, lower atmospheric visibility, and camera stability. in this paper, we compare performance and saliency of state of the art place recognition on both terrestrial imagery and waterborne imagery from the Symphony Lake dataset, to see how capable modern pipelines are at adapting to the latter. We utilize convolutional neural network features to highlight salient regions of the candidate image that contributed to its retrieval to gain further insight into what key features are being extracted for each.