  With the emergence of LLMs and their integration with other data modalities,
multi-modal 3D perception attracts more attention due to its connectivity to
the physical world and makes rapid progress. However, limited by existing
datasets, previous works mainly focus on understanding object properties or
inter-object spatial relationships in a 3D scene. To tackle this problem, this
paper builds the first largest ever multi-modal 3D scene dataset and benchmark
with hierarchical grounded language annotations, MMScan. It is constructed
based on a top-down logic, from region to object level, from a single target to
inter-target relationships, covering holistic aspects of spatial and attribute
understanding. The overall pipeline incorporates powerful VLMs via carefully
designed prompts to initialize the annotations efficiently and further involve
humans' correction in the loop to ensure the annotations are natural, correct,
and comprehensive. Built upon existing 3D scanning data, the resulting
multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects
and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding
and question-answering benchmarks. We evaluate representative baselines on our
benchmarks, analyze their capabilities in different aspects, and showcase the
key problems to be addressed in the future. Furthermore, we use this
high-quality dataset to train state-of-the-art 3D visual grounding and LLMs and
obtain remarkable performance improvement both on existing benchmarks and
in-the-wild evaluation. Codes, datasets, and benchmarks will be available at
https://github.com/OpenRobotLab/EmbodiedScan.
