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.

翻译：近年来，由于其在计算机视觉、计算机图形学、虚拟现实和增强现实等领域的广泛应用，3D物体检索的重要性显著提升。然而，由于3D模型的复杂性——其形状、尺寸、纹理各异且包含大量多边形和顶点——使得3D物体检索面临重大挑战。为此，我们提出了一项新的SHREC挑战任务，该任务专注于通过草图查询从数据集中检索相关3D动物模型，并借助现有草图加速访问3D模型。此外，本研究构建了一个名为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。本竞赛要求参赛者根据复杂且详细的草图检索3D模型。我们收到了来自8个团队的204次运行结果，成绩令人满意。尽管仍需进一步改进，但所提出的任务具有激励3D物体检索领域更多研究的潜力，有望为广泛应用带来益处。我们还对未来潜在的研究方向提出了见解，例如改进特征提取与匹配技术，以及构建更丰富的数据集以评估检索性能。