Empowering autonomous agents with 3D understanding for daily objects is a grand challenge in robotics applications. When exploring in an unknown environment, existing methods for object pose estimation are still not satisfactory due to the diversity of object shapes. In this paper, we propose a novel framework for category-level object shape and pose estimation from a single RGB-D image. To handle the intra-category variation, we adopt a semantic primitive representation that encodes diverse shapes into a unified latent space, which is the key to establish reliable correspondences between observed point clouds and estimated shapes. Then, by using a SIM(3)-invariant shape descriptor, we gracefully decouple the shape and pose of an object, thus supporting latent shape optimization of target objects in arbitrary poses. Extensive experiments show that the proposed method achieves SOTA pose estimation performance and better generalization in the real-world dataset. Code and video are available at https://zju3dv.github.io/gCasp.

翻译：赋予自主智能体对日常物体的三维理解能力是机器人应用中的重大挑战。在未知环境中探索时，由于物体形状的多样性，现有物体姿态估计方法仍不理想。本文提出一种新颖框架，通过单张RGB-D图像实现类别级物体形状与姿态估计。为处理类别内变异，我们采用语义基元表示，将多样形状编码至统一潜在空间，这是建立观测点云与估计形状间可靠对应关系的关键。随后，通过使用SIM(3)不变形状描述子，优雅地解耦物体形状与姿态，从而支持任意姿态下目标物体的潜在形状优化。大量实验表明，所提方法在真实数据集上实现了最先进的姿态估计性能与更强的泛化能力。代码与视频见https://zju3dv.github.io/gCasp。