BOP-Distrib: 重新审视6D位姿估计基准测试以在视觉模糊情况下获得更好的评估
BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities
AI总结：
BOP-Distrib重新审视6D位姿估计基准测试，通过自动生成图像级位姿分布来改进评估，在视觉模糊情况下提升方法评估的准确性。
摘要：
6D位姿估计旨在确定最能解释相机观测的物体位姿。对于非模糊物体的唯一解，当视点导致对称物体或对称破坏元素的遮挡时，可能会变成多模态位姿分布。目前，6D位姿估计方法在基准数据集上进行评估，这些数据集将视觉模糊性视为仅与全局物体对称性相关，而应按图像进行定义以考虑相机视点。因此，我们首先提出一种自动方法，重新标注这些数据集，为每个图像生成特定的6D位姿分布，考虑物体表面在图像中的可见性以正确确定视觉模糊性。其次，鉴于此改进的地面真实数据，我们重新评估了最先进的单位姿方法，并表明这大大改变了这些方法的排名。第三，由于一些近期工作专注于估计完整的解集，我们推导出一种精度/召回率公式，以我们的图像级分布地面真实数据评估它们，使其成为首个在真实图像上对位姿分布方法进行基准测试的基准。
英文摘要：
6D pose estimation aims at determining the object pose that best explains the camera observation. The unique solution for non-ambiguous objects can turn into a multi-modal pose distribution for symmetrical objects or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries, whereas they should be defined per-image to account for the camera viewpoint. We thus first propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the object surface visibility in the image to correctly determine the visual ambiguities. Second, given this improved ground truth, we re-evaluate the state-of-the-art single pose methods and show that this greatly modifies the ranking of these methods. Third, as some recent works focus on estimating the complete set of solutions, we derive a precision/recall formulation to evaluate them against our image-wise distribution ground truth, making it the first benchmark for pose distribution methods on real images.
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