Cryo-EM is a vital technique for determining 3D structure of biological
molecules such as proteins and viruses. The cryo-EM reconstruction problem is
challenging due to the high noise levels, the missing poses of particles, and
the computational demands of processing large datasets. A promising solution to
these challenges lies in the use of amortized inference methods, which have
shown particular efficacy in pose estimation for large datasets. However, these
methods also encounter convergence issues, often necessitating sophisticated
initialization strategies or engineered solutions for effective convergence.
Building upon the existing cryoAI pipeline, which employs a symmetric loss
function to address convergence problems, this work explores the emergence and
persistence of these issues within the pipeline. Additionally, we explore the
impact of equivariant amortized inference on enhancing convergence. Our
investigations reveal that, when applied to simulated data, a pipeline
incorporating an equivariant encoder not only converges faster and more
frequently than the standard approach but also demonstrates superior
performance in terms of pose estimation accuracy and the resolution of the
reconstructed volume. Notably, $D_4$-equivariant encoders make the symmetric
loss superfluous and, therefore, allow for a more efficient reconstruction
pipeline.

冷冻电子显微术是一种确定蛋白质和病毒等生物分子三维结构的重要技术。本文探索了 cryoAI 管道中收敛问题的出现和持久性以及等变的摊解推理对增强收敛的影响，研究发现在模拟数据上，集成了等变编码器的管道不仅收敛更快、更频繁，而且在姿态估计准确度和重建体积的分辨率方面表现出优越性。值得注意的是，$D_4$- 等变编码器使对称损失变得多余，从而允许更高效的重建管道。