  Recent research has evidenced the significant potentials of Large Language
Models (LLMs) in handling challenging tasks within 3D scenes. However, current
models are constrained to addressing object-centric tasks, where each
question-answer pair focuses solely on an individual object. In real-world
applications, users may pose queries involving multiple objects or expect for
answers that precisely reference various objects. We introduce the use of
object identifiers to freely reference objects during a conversation. While
this solution appears straightforward, it presents two main challenges: 1) How
to establish a reliable one-to-one correspondence between each object and its
identifier? 2) How to incorporate complex spatial relationships among dozens of
objects into the embedding space of the LLM? To address these challenges, we
propose a two-stage alignment method, which involves learning an
attribute-aware token and a relation-aware token for each object. These tokens
capture the object's attributes and spatial relationships with surrounding
objects in the 3D scene. Once the alignment is established, we can fine-tune
our model on various downstream tasks using instruction tuning. Experiments
conducted on traditional datasets like ScanQA, ScanRefer, and Nr3D/Sr3D
showcase the effectiveness of our proposed method. Additionally, we create a 3D
scene captioning dataset annotated with rich object identifiers, with the
assistant of GPT-4. This dataset aims to further explore the capability of
object identifiers in effective object referencing and precise scene
understanding.
