  In this work, we present Uni3DL, a unified model for 3D and Language
understanding. Distinct from existing unified vision-language models in 3D
which are limited in task variety and predominantly dependent on projected
multi-view images, Uni3DL operates directly on point clouds. This approach
significantly expands the range of supported tasks in 3D, encompassing both
vision and vision-language tasks in 3D. At the core of Uni3DL, a query
transformer is designed to learn task-agnostic semantic and mask outputs by
attending to 3D visual features, and a task router is employed to selectively
generate task-specific outputs required for diverse tasks. With a unified
architecture, our Uni3DL model enjoys seamless task decomposition and
substantial parameter sharing across tasks. Uni3DL has been rigorously
evaluated across diverse 3D vision-language understanding tasks, including
semantic segmentation, object detection, instance segmentation, visual
grounding, 3D captioning, and text-3D cross-modal retrieval. It demonstrates
performance on par with or surpassing state-of-the-art (SOTA) task-specific
models. We hope our benchmark and Uni3DL model will serve as a solid step to
ease future research in unified models in the realm of 3D and language
understanding. Project page: https://uni3dl.github.io.
