  The ability to visually re-identify objects is a fundamental capability in
vision systems. Oftentimes, it relies on collections of visual signatures based
on descriptors, such as SIFT or SURF. However, these traditional descriptors
were designed for a certain domain of surface appearances and geometries
(limited relief). Consequently, highly-textured surfaces such as tree bark pose
a challenge to them. In turn, this makes it more difficult to use trees as
identifiable landmarks for navigational purposes (robotics) or to track felled
lumber along a supply chain (logistics). We thus propose to use data-driven
descriptors trained on bark images for tree surface re-identification. To this
effect, we collected a large dataset containing 2,400 bark images with strong
illumination changes, annotated by surface and with the ability to pixel-align
them. We used this dataset to sample from more than 2 million 64x64 pixel
patches to train our novel local descriptors DeepBark and SqueezeBark. Our
DeepBark method has shown a clear advantage against the hand-crafted
descriptors SIFT and SURF. For instance, we demonstrated that DeepBark can
reach a mAP of 87.2% when retrieving 11 relevant bark images, i.e.
corresponding to the same physical surface, to a bark query against 7,900
images. Our work thus suggests that re-identifying tree surfaces in a
challenging illuminations context is possible. We also make public our dataset,
which can be used to benchmark surface re-identification techniques.
