RobustMat: Neural Diffusion for Street Landmark Patch Matching under
  Challenging Environments
Rui She Qiyu Kang Sijie Wang Yuan-Rui Yang Kai Zhao Yang Song Wee Peng Tay
For autonomous vehicles (AVs), visual perception techniques based on sensors
like cameras play crucial roles in information acquisition and processing. In
various computer perception tasks for AVs, it may be helpful to match landmark
patches taken by an onboard camera with other landmark patches captured at a
different time or saved in a street scene image database. To perform matching
under challenging driving environments caused by changing seasons, weather, and
illumination, we utilize the spatial neighborhood information of each patch. We
propose an approach, named RobustMat, which derives its robustness to
perturbations from neural differential equations. A convolutional neural ODE
diffusion module is used to learn the feature representation for the landmark
patches. A graph neural PDE diffusion module then aggregates information from
neighboring landmark patches in the street scene. Finally, feature similarity
learning outputs the final matching score. Our approach is evaluated on several
street scene datasets and demonstrated to achieve state-of-the-art matching
results under environmental perturbations.