Complementary Bi-directional Feature Compression for Indoor 360{\deg} Semantic Segmentation with Self-distillation

Understand

Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction.

• However, these methods ignore the distortion distribution prior and are limited to unbalanced receptive fields, e.g., the receptive fields are sufficient in the vertical direction and insufficient in the horizontal direction.
• Differently, a vertical representation compressed in another direction can offer implicit distortion prior and enlarge horizontal receptive fields.
• In this paper, we combine the two different representations and propose a novel 360{\deg} semantic segmentation solution from a complementary perspective.