The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can vary in shape, size, and texture, and have numerous polygons and vertices. To this end, we introduce a novel SHREC challenge track that focuses on retrieving relevant 3D animal models from a dataset using sketch queries and expedites accessing 3D models through available sketches. Furthermore, a new dataset named ANIMAR was constructed in this study, comprising a collection of 711 unique 3D animal models and 140 corresponding sketch queries. Our contest requires participants to retrieve 3D models based on complex and detailed sketches. We receive satisfactory results from eight teams and 204 runs. Although further improvement is necessary, the proposed task has the potential to incentivize additional research in the domain of 3D object retrieval, potentially yielding benefits for a wide range of applications. We also provide insights into potential areas of future research, such as improving techniques for feature extraction and matching and creating more diverse datasets to evaluate retrieval performance. https://aichallenge.hcmus.edu.vn/sketchanimar

翻译：近年来，由于3D对象检索在计算机视觉、计算机图形学、虚拟现实和增强现实等领域的广泛应用，其重要性日益凸显。然而，3D模型因形状、尺寸和纹理各异，且包含众多多边形与顶点，使得3D对象检索面临重大挑战。为此，我们提出一个新的SHREC挑战赛，专注于通过草图查询从数据集中检索相关3D动物模型，并利用已有草图加速3D模型访问。此外，本研究构建了名为ANIMAR的新数据集，包含711个独特的3D动物模型和140个对应的草图查询。本竞赛要求参赛者基于复杂且精细的草图检索3D模型。我们从八个团队和204次运行中获得了令人满意的结果。尽管仍需进一步改进，但所提出的任务有望激励3D对象检索领域的更多研究，从而可能为广泛的应用带来益处。我们还对未来的研究方向提供了见解，例如改进特征提取与匹配技术，以及创建更多样化的数据集以评估检索性能。网址：https://aichallenge.hcmus.edu.vn/sketchanimar