SpatialLM: Training Large Language Models for Structured Indoor Modeling
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Abstract (arXiv)
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.
Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Builds on 62 of its 69 references that are in the map. First posted on arXiv 9 Jun 2025.
Builds on
62 of 69 references.
References · 62
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- 2024SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videoscited by 100
- 2021FCAF3D: Fully Convolutional Anchor-Free 3D Object Detectioncited by 97
- 2024SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Modelcited by 95
- 2024Apollo: An Exploration of Video Understanding in Large Multimodal Modelscited by 87
50 most cited of 62 All 62 (JSON)
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Citing papers per year: 2025: 21; 2026: 42.
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- 2026Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models4 Jun 2026
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- 20263D Segmentation Using Viewpoint-Dependent Spatial Relationships15 May 2026
- 2026SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs10 May 2026
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