TextANIMAR: Text-based 3D animal fine-grained retrieval.

3D object retrieval is an important yet challenging task that has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe this task can potentially drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from fully solved. As such, we provide insights into potential areas for future research and improvements. We believe we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. • ANIMAR dataset is created with 711 3D animal models and 150 text queries. • SHREC challenge track focusing on Text-based 3D ANIMAl model fine-grained Retrieval. • Five groups participated in our competition, submitting a total of 114 runs. • We push boundaries of 3D object retrieval and facilitate user-friendly interactions. [ABSTRACT FROM AUTHOR]