Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typically establish one-to-one correspondences between image pixels and 3D object surface vertices. However, the utilization of one-to-one correspondences introduces ambiguity for symmetric objects. To address this, we propose SymCode, a symmetry-aware surface encoding that encodes the object surface vertices based on one-to-many correspondences, eliminating the problem of one-to-one correspondence ambiguity. We also introduce SymNet, a fast end-to-end network that directly regresses the 6D pose parameters without solving a PnP problem. We demonstrate faster runtime and comparable accuracy achieved by our method on the T-LESS and IC-BIN benchmarks of mostly symmetric objects. Our source code will be released upon acceptance.

翻译：从单张RGB图像估计物体的6D姿态是一项关键任务，当处理对称物体时，这一任务会变得更具挑战性。现有方法通常建立图像像素与3D物体表面顶点之间的一一对应关系。然而，对于对称物体，一一对应关系的使用会引入歧义性。为了解决这一问题，我们提出了SymCode，一种对称感知的表面编码方法，它基于一对多对应关系对物体表面顶点进行编码，从而消除了一一对应歧义的问题。我们还引入了SymNet，一种快速的端到端网络，能够直接回归6D姿态参数，无需解决PnP问题。我们在主要由对称物体组成的T-LESS和IC-BIN基准测试中展示了更快的运行时间和可比的精度。我们的源代码将在论文被接收后公开发布。