Diff-COPE: Diffusion-Based Category-Level 6D Object Pose Estimation

Category-level 6D object pose estimation has gained increasing attention in applications of robotic manipulation, augmented reality, and scene understanding, due to its ability to generalize to unseen instances within the same category. However, existing methods struggle with handling the intra-class shape variations as they either adopt mean shape as priors, or build the associations among different instances without explicit category-shared information. To address this problem, a novel category-level object pose estimation method based on diffusion model is proposed, which utilize the generative ability of diffusion model to refine a sparse categorical representation. In contrast to existing dense correspondence-based methods, our method employs a set of keypoints provided by learnable queries to represent object shape, enabling better categorical representation of different instances by focusing on the representative object components. The keypoints are then refined through a forward diffusion process and a reverse denoising process conditioned on category information. This allows the flexible adaption to various instances, especially for those that deviate from the mean shape within the same category. On this basis, a geometric-semantic feature fusion module is presented to enhance keypoint feature representation. By integrating the geometric information from point cloud with the high-level semantics from RGB image using a two-branch attention mechanism, the keypoint feature is enriched and deeply combined, which facilitates the subsequent pose estimation. Extensive experiments on the REAL275 dataset, the CAMERA25 dataset, and real-world complex scenarios demonstrated the effectiveness of proposed method.