MagicCubePose, A more comprehensive 6D pose estimation network

Most of the current mainstream 6D pose estimation methods use template or voting-based methods. Such methods are usually multi-stage or have multiple assumptions and post-correction, which will cause a certain degree of information redundancy and increase the computational cost, their real-time detection performance is poor. We point out that traditional path aggregation networks introduce new errors, therefore, we propose a loss function: MagicCubeLoss, a portable module: MagicCubeNet, and the corresponding 6D pose estimation model: MagicCubePose. MagicCubePose has good expansion performance and can build more efficient models for different calculation power and scenarios. Experiments show that our model has good real-time detection performance and the highest ADD(-S) accuracy.