In this paper, we address a problem of correcting upright orientation of a reconstructed object to search. We first reconstruct an input object appearing in an image sequence, and generate a query shape using multi-view object co-segmentation. In the next phase, we utilize the Convolutional Neural Network (CNN) architecture to determine category-specific upright orientation of the queried shape for 3D model classification and retrieval. As a practical application of our system, a shape style and a pose from an inferred category and up-vector are obtained by comparing 3D shape similarity with candidate 3D models and aligning its projections with a set of 2D co-segmentation masks. We quantitatively and qualitatively evaluate the presented system with more than 720 upfront-aligned 3D models and five sets of multi-view image sequences.

在本文中，我们解决了校正要搜索的重建对象的垂直方向的问题。我们首先重建出现在图像序列中的输入对象，然后使用多视图对象共同细分生成查询形状。在下一阶段，我们将使用卷积神经网络（CNN）体系结构来确定用于3D模型分类和检索的查询形状的特定于类别的直立方向。作为我们系统的实际应用，可以通过将3D形状相似性与候选3D模型进行比较并将其投影与一组2D共分割蒙版对齐来从推断的类别和上矢量获得形状样式和姿势。我们用720多个前期对齐的3D模型和五组多视图图像序列对本系统进行了定量和定性评估。