SpatialLM: Training Large Language Models for Structured Indoor Modeling
Authors: name: Yongsen Mao, name: Junhao Zhong, name: Chuan Fang, name: Jia Zheng, name: Rui Tang, name: Hao Zhu, name: Ping Tan, name: Zihan Zhou
Organization: Manycore Research

SpatialLM is a large language model designed to process 3D point cloud data
and generate structured 3D scene understanding outputs. These outputs include
architectural elements like walls, doors, windows, and oriented object boxes
with their semantic categories. Unlike previous methods which exploit
task-specific network designs, our model adheres to the standard multimodal LLM
architecture and is fine-tuned directly from open-source LLMs.
  To train SpatialLM, we collect a large-scale, high-quality synthetic dataset
consisting of the point clouds of 12,328 indoor scenes (54,778 rooms) with
ground-truth 3D annotations, and conduct a careful study on various modeling
and training decisions. On public benchmarks, our model gives state-of-the-art
performance in layout estimation and competitive results in 3D object
detection. With that, we show a feasible path for enhancing the spatial
understanding capabilities of modern LLMs for applications in augmented
reality, embodied robotics, and more.