Holistic approaches regress the 3D translation and orientation components of the target object directly from the given input **data** [ designed a novel multi-modal feature fusion framework, which utilizes two feature embedding branches to respectively extract the color and the geometry cues, and employs a pixel-wise feature fusion mechanism to achieve deep interaction of the multi-modality data. This line of work can achieve satisfactory performance in several benchmarks and be capable to implement end-to-end network architecture directly. However, the generalization and learnability of these methods are restricted by the non-linearity of rotation space.