Title: iCaps: Iterative Category-level Object Pose and Shape Estimation Show Chinese title

Title: iCaps：迭代类别级物体位姿和形状估计

Abstract: This paper proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category. By exploiting an implicit shape representation based on signed distance functions, we build a LatentNet to estimate a latent representation of the 3D shape given the estimated pose of an object. Then the estimated pose and shape can be used to update each other in an iterative way. Our category-level 6D object pose and shape estimation pipeline only requires 2D detection and segmentation for initialization. We evaluate our approach on a publicly available dataset and demonstrate its effectiveness. In particular, our method achieves comparably high accuracy on shape estimation. Show Chinese abstract

Abstract: 本文提出了一种类别级别的6D物体位姿和形状估计方法iCaps，该方法允许跟踪类别中未见过的物体的6D位姿并估计其3D形状。 我们开发了一个类别级别的自编码器网络，使用深度图像作为输入，其中自编码器的特征嵌入编码了类别中物体的位姿。 自编码器可以用于粒子滤波框架中，以估计和跟踪类别中物体的6D位姿。 通过利用基于符号距离函数的隐式形状表示，我们构建了一个LatentNet，给定物体的估计位姿来估计其3D形状的潜在表示。 然后，估计的位姿和形状可以以迭代的方式相互更新。 我们的类别级别6D物体位姿和形状估计流程仅需要2D检测和分割进行初始化。 我们在一个公开的数据集上评估了我们的方法，并证明了其有效性。 特别是，我们的方法在形状估计方面达到了相当高的精度。