用扩散模型估算单张图中类别物体的6D位姿，无需深度信息或特定模型。
Category Level 6D Object Pose Estimation from a Single RGB Image using Diffusion
- 基于得分扩散模型生成位姿假设，建模可能位姿分布。
- 在REAL275数据集上显著超越当前最先进方法。
- 采用均值漂移简化位姿聚合，无需复杂似然估计器。
从图像中估计物体的6D位姿和3D尺寸是计算机视觉的基础任务。现有方法多局限于已知模型的特定实例，或依赖真实深度信息或激光雷达点云。本文解决从单张RGB图像进行类别级物体位姿估计的难题，提出新方法，无需特定物体模型或深度信息。该方法利用基于得分的扩散模型生成物体位姿假设，以建模可能位姿的分布。不同于以往依赖昂贵训练的似然估计器去除异常值后使用均值池化聚合位姿，本文引入更简单的均值漂移方法，直接估计分布的众数作为最终位姿。实验表明，该方法在REAL275数据集上显著优于当前最先进水平。
原文摘要 · Abstract (English)
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.
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