  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.
https://aichallenge.hcmus.edu.vn/textanimar
