Systematic Evaluation of Novel View Synthesis for Video Place Recognition
This work addresses the problem of enhancing robot navigation through novel view synthesis, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper systematically evaluated the impact of synthetic novel views on Video Place Recognition (VPR) using five public databases and seven image similarity methods, finding that small additions improve recognition statistics, while larger additions depend more on the number of views and dataset imagery than viewpoint magnitude.
The generation of synthetic novel views has the potential to positively impact robot navigation in several ways. In image-based navigation, a novel overhead view generated from a scene taken by a ground robot could be used to guide an aerial robot to that location. In Video Place Recognition (VPR), novel views of ground locations from the air can be added that enable a UAV to identify places seen by the ground robot, and similarly, overhead views can be used to generate novel ground views. This paper presents a systematic evaluation of synthetic novel views in VPR using five public VPR image databases and seven typical image similarity methods. We show that for small synthetic additions, novel views improve VPR recognition statistics. We find that for larger additions, the magnitude of viewpoint change is less important than the number of views added and the type of imagery in the dataset.