Gauge-Equivariant Attention for Rotation-Stable 360° Scene Understanding (EquiSSL)
ACM Transactions on Graphics, Vol. 45, No. 6, Article 242 · SIGGRAPH Asia 2026 Journal Track
TL;DR — Spherical transformers lose more than half their segmentation accuracy when the camera rotates by 90°. EquiSSL removes the gauge dependence of the attention bias with a parameter-free Reynolds average, cutting that drop to 0.8% and turning rotation-consistent self-supervision into a label-efficiency lever.
一句话总结——球面 Transformer 在相机旋转 90° 时会损失过半的分割精度。EquiSSL 以无参数的 Reynolds 平均消除注意力偏置的规范依赖,将精度下降降至 0.8%,并把旋转一致的自监督变成提升标签效率的杠杆。
@article{zhou2026gauge,
title = {Gauge-Equivariant Attention for Rotation-Stable 360$^\circ$ Scene Understanding},
author = {Zhou, Tianjian and Li, Yishan and Jiang, Jie and Zhang, Yifei},
journal = {ACM Transactions on Graphics},
volume = {45},
number = {6},
year = {2026},
note = {Article 242},
doi = {10.1145/3842525}
}