Rotation Representation

Rotation representation in machine learning focuses on efficiently and accurately representing 3D rotations for various applications, such as object pose estimation and human pose modeling. Current research emphasizes developing novel representations, including Gaussian splatting for improved rendering, and exploring alternative approaches like unorthogonalized matrices to accelerate training and enhance accuracy. These advancements are crucial for improving the performance of computer vision and robotics systems that rely on accurate and robust 3D rotation estimation, leading to more efficient and effective algorithms in these fields.