Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects from a specific category. Thanks to prior deformation, which explicitly adapts a category-specific 3D prior (i.e., a 3D template) to a given object instance, prior-based methods attained great success and have become a major research stream. However, obtaining category-specific priors requires collecting a large amount of 3D models, which is labor-consuming and often not accessible in practice. This motivates us to investigate whether priors are necessary to make prior-based methods effective. Our empirical study shows that the 3D prior itself is not the credit to the high performance. The keypoint actually is the explicit deformation process, which aligns camera and world coordinates supervised by world-space 3D models (also called canonical space). Inspired by these observation, we introduce a simple prior-free implicit space transformation network, namely IST-Net, to transform camera-space features to world-space counterparts and build correspondence between them in an implicit manner without relying on 3D priors. Besides, we design camera- and world-space enhancers to enrich the features with pose-sensitive information and geometrical constraints, respectively. Albeit simple, IST-Net becomes the first prior-free method that achieves state-of-the-art performance, with top inference speed on the REAL275 dataset. Our code and models will be publicly available.

翻译：类别级6D姿态估计旨在预测特定类别中未见物体的姿态与尺寸。得益于先验形变——即显式地将类别特定的3D先验（如3D模板）适配到给定物体实例，基于先验的方法取得了巨大成功，并已成为主流研究方向。然而，获取类别特定的先验需要收集大量3D模型，这一过程费时费力且在实际中往往难以实现。这促使我们探究先验是否是使基于先验的方法有效的必要条件。实验表明，3D先验本身并非高精度性能的关键，真正的关键在于显式形变过程——该过程通过世界空间3D模型（亦称规范空间）的监督，实现相机坐标系与世界坐标系的对齐。基于上述观察，我们提出一种简洁的无先验隐式空间变换网络IST-Net，该网络无需依赖3D先验，即可将相机空间特征隐式转换为世界空间特征，并建立两者间的对应关系。此外，我们设计了相机空间增强器与世界空间增强器，分别赋予特征姿态敏感信息与几何约束。尽管结构简单，IST-Net成为首个在REAL275数据集上达到最先进性能且推理速度最快的无先验方法。我们的代码与模型将公开发布。