Orientation-aware semantic segmentation on icosahedron spheres

Abstract

We address semantic segmentation on omnidirectional images, to leverage a holistic understanding of the surrounding scene for applications like autonomous driving systems. For the spherical domain, several methods recently adopt an icosahedron mesh, but systems are typically rotation invariant or require significant memory and parameters, thus enabling execution only at very low resolutions. In our work, we propose an orientation-aware CNN framework for the icosahedron mesh. Our representation allows for fast network operations, as our design simplifies to standard network operations of classical CNNs, but under consideration of north-aligned kernel convolutions for features on the sphere. We implement our representation and demonstrate its memory efficiency upto a level-8 resolution mesh (equivalent to 640×1024 equirectangular images). Finally, since our kernels operate on the tangent of the sphere, standard feature weights, pretrained on perspective data, can be directly transferred with only small need for weight re- finement. In our evaluation our orientation-aware CNN becomes a new state of the art for the recent 2D3DS dataset, and our Omni-SYNTHIA version of SYNTHIA. Rotation invariant classification and segmentation tasks are addition- ally presented for comparison to prior art.

Publication
In International Conference on Computer Vision 2019
Given spherical input, we convert it to an unfolded icosahedron mesh. Hexagonal filters are then applied under consideration of north alignment, as we efficiently interpolate vertices. Our approach is suited to most classical CNN architectures, e.g. U-Net. Since we work with spherical data, final segmentation results provide a holistic labeling of the environment.
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Chao Zhang
Computer Vision Researcher
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Will Smith
Reader in Computer Vision