We approach the problem by considering semantics as the formalised knowledge related to a category of objects the geometry can vary provided that the semantics is preserved. Changing geometry without semantic clues may invalidate such functionalities or the meaning of objects or their parts. However, 3D models have a structure, given by the arrangement of salient parts, and shape and structure are deeply related to semantics and functionality. Even if different attempts have been done in this direction, shape modelling still mainly focuses on geometry. The evolution of 3D visual content calls for innovative methods for modelling shapes based on their intended usage, function and role in a complex scenario. Compared with the traditional costume restoration methods, the method proposed in this paper reduces the technical requirements for operators in the restoration process without destroying cultural relics, and provides a new method for the rapid simulation and restoration of ancient Chinese costumes. Finally, we create a fuzzy comprehensive evaluation of the restoration effect of the restored costumes. We use 3D virtual simulation technology to sew the patterns of Tang Dynasty tomb mural costumes and realize the virtual simulation restoration of Tang Dynasty tomb mural costumes. Then, the structural curves of the garment are drawn on the 3D garment according to the characteristics of the Tang Dynasty tomb mural garment style, the 3D surface is expanded and surrounded by these curves into the 2D garment plane, and the expanded surface is adjusted to obtain the 2D garment plane pattern. Firstly, the 3D garment model is constructed in the virtual environment according to the costume outline of the Tang Dynasty tomb mural costume. This paper takes the tomb murals as the research object, and realizes the development of the costume patterns of the Tang tomb murals and the 3D simulation restoration of the costumes through 3D interactive clothing pattern-making technology and virtual simulation technology. It proves that the proposed end-to-end learning network is more effective and suitable for the classification of the Terracotta Warrior fragments. The robustness and effectiveness of the performance on the task of 3D point cloud classification are also investigated. For real-world data sets, the accuracy is best among the existing methods. The accuracy results with normal can achieve 93.52% and 96.22%, respectively. Extensive experiments on the public data set (ModelNet40/10) and the real-world Terracotta Warrior fragments data set are conducted. IMLP can concatenate the high-level and low-level features for classification tasks. With a multi-scale strategy, LGLayer can parallel extract the local and global features from different scales. MS-BLOCK consists of a local-global layer (LGLayer) and an improved multi-layer perceptron (IMLP). AMS-Net is a hierarchical structure consisting of a multi-scale set abstraction block (MS-BLOCK) and a fully connected (FC) layer. In this case, an attention-based multi-scale neural network named AMS-Net is proposed to extract significant geometric and semantic features. A practical and effective classification method for fragments is an urgent need. However, most of the existing methods are based on traditional technology and have low accuracy in classification. Moreover, the proposed method can significantly increase the efficiency of future fragment reassembly for the Terracotta Warriors.ДЄs an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. The promising results demonstrate that the method is the most accurate technique for classifying 3D Terracotta Warrior fragments to date. Extensive experiments were performed to verify the effectiveness of the proposed method. Then, misclassified fragments are secondly categorized based on their best match to a complete Terracotta Warrior model. The fragments are initially classified using PointNet. In this study, an automatic method based on a deep learning network combined with template guidance is proposed to classify 3D fragments of the Terracotta Warriors.

Fragment classification is currently performed manually on enormous quantities of fragments, which is a time-consuming, inaccurate, and subjective task for archaeologists and conservators. However, these relics have been predominantly found in fragments. They are among the most precious unearthed cultural relics of China. The Terracotta Warriors are terracotta sculptures created for ChinaвЂ™s first emperor more than 2,000 years ago.

Terracotta fragments meaning