Category Level 6D Object Pose Estimation from a Single RGB Image using Diffusion
This addresses a fundamental computer vision problem for robotics and AR/VR applications, representing a novel method rather than incremental improvement.
The paper tackles category-level 6D object pose estimation from a single RGB image without requiring object models or depth information, using diffusion models to generate pose hypotheses and Mean Shift for aggregation, and reports outperforming state-of-the-art on the REAL275 dataset.
Estimating the 6D pose and 3D size of an object from an image is a fundamental task in computer vision. Most current approaches are restricted to specific instances with known models or require ground truth depth information or point cloud captures from LIDAR. We tackle the harder problem of pose estimation for category-level objects from a single RGB image. We propose a novel solution that eliminates the need for specific object models or depth information. Our method utilises score-based diffusion models to generate object pose hypotheses to model the distribution of possible poses for the object. Unlike previous methods that rely on costly trained likelihood estimators to remove outliers before pose aggregation using mean pooling, we introduce a simpler approach using Mean Shift to estimate the mode of the distribution as the final pose estimate. Our approach outperforms the current state-of-the-art on the REAL275 dataset by a significant margin.