论文标题
关于基于RGB-D的分类姿势和形状估计的评估
On the Evaluation of RGB-D-based Categorical Pose and Shape Estimation
论文作者
论文摘要
最近，已经提出了对物体的6D姿势和形状估计的各种方法。通常，这些方法根据平均精度评估其姿势估计，并以倒角距离进行重建质量。在这项工作中，我们仔细研究了包括指标和数据集在内的主要评估协议。我们提出了一组新的指标，为红木数据集提供了新的注释，并在公平的比较中评估了最先进的方法。我们发现，现有方法并不能很好地推广到不受约束的方向，并且实际上对物体正直有很大的偏见。我们用定义明确的指标，方法和数据集接口构成了易于使用的评估工具箱，该工具盒易于与各种Enta-t-the-the-ART方法进行评估和比较（请参阅https://github.com/roym899/pose_and_and_and_shape_evaluation）。
Recently, various methods for 6D pose and shape estimation of objects have been proposed. Typically, these methods evaluate their pose estimation in terms of average precision, and reconstruction quality with chamfer distance. In this work we take a critical look at this predominant evaluation protocol including metrics and datasets. We propose a new set of metrics, contribute new annotations for the Redwood dataset and evaluate state-of-the-art methods in a fair comparison. We find that existing methods do not generalize well to unconstrained orientations, and are actually heavily biased towards objects being upright. We contribute an easy-to-use evaluation toolbox with well-defined metrics, method and dataset interfaces, which readily allows evaluation and comparison with various state-of-the-art approaches (see https://github.com/roym899/pose_and_shape_evaluation ).
