GeoAnchor: Collaborative Reasoning via Latent Decomposition for 3D Spatial Understanding

GeoMind: Explicit Spatial Reasoning via Dual-Reference Geometric Modeling

GraFT: A Training-Free Framework for Spatial Reasoning in Multimodal Large Language Models via 3D Scene Graphs

3D spatial reasoning underpins understanding and acting in the physical world, yet it remains unreliable in current multimodal large language models (MLLMs). These models falter at precise geometric measurement, at transforming between egocentric and allocentric viewpoints, and at grounding fine-grained appearance. The most common remedies fine-tune the model on large-scale curated spatial-reasoning datasets or attach dedicated encoders for 3D geometry, which typically couples the solution to costly supervision and a specific backbone. We instead introduce GraFT, a training-free framework that supplies the missing 3D structure through a compact, easily maintained 3D scene graph (3DSG). From this 3DSG, GraFT provides three spatial reasoning capabilities: (1) deterministic geometry through symbolic tools, (2) allocentric layout through a bird's-eye-view (BEV) rendering, and (3) visual-attribute grounding through task-relevant egocentric frames. On ScanQA, GraFT improves every metric over the same-backbone baseline, raising CIDEr by 27%. On VSI-Bench, GraFT improves frozen MLLMs by up to 65%, surpassing every proprietary and general-purpose open-source baseline, and several prominent fine-tuned spatial models.

Qwen-3D: A Generalist 3D Vision-Language Model for Spatial Understanding

Qwen-3D is introduced, a geometry-aware LMM that compresses visual information within the Qwen backbone using multi-view geometric cues, enabling efficient long-horizon visual reasoning over static scenes and incorporates a query-based segmentation decoder that grounds language directly in the underlying 3D scene representation.

Geometry-Aware Test-Time Learning for Quantitative Spatial Reasoning

Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space from a 2D image and a natural language query. Despite recent progress, VLM spatial reasoning remains brittle under distribution shifts, largely due to the high cost of 3D supervision. As a result, models often produce inconsistent or contradictory predictions when faced with novel object configurations or rephrased spatial queries, revealing a misalignment between learned representations and underlying geometry. To address this, we propose TTL-SR, a geometry-aware Test-Time Learning framework for quantitative Spatial Reasoning that leverages geometric consistency constraints and unlabeled test data to adapt models to target domains. Specifically, TTL-SR augments the input query with geometrically coupled auxiliary queries, filters unreliable predictions via adaptive geometric triggering to construct structured token-level pseudo-labels, and updates model parameters under a geometry-aware multi-objective loss using only test data. Experimental results demonstrate that TTL-SR significantly boosts spatial reasoning performance, yielding 6.47% and 9.41% accuracy gains for Qwen3-VL-4B-Instruct and SpatialRGPT-VILA-1.5-8B on Q-Spatial-ScanNet dataset, respectively.

3D vision-language question answering with explicit scene graphs and local topology priors

TA-LMM, a 3D visual question answering method built on explicit scene graphs and local topological priors, is proposed, suggesting that explicit local topological priors can improve scene consistency in 3D visual question answering.

Enhancing Spatial Understanding in Vision-Language Models via Curriculum Learning

The development of Embodied AI urgently necessitates high-fidelity environment modeling enriched with spatial context. However, existing 3D semantic scene understanding methods predominantly focus on isolated instance-level labels or high-dimensional semantic vector embeddings, lacking effective modeling of explicit spatial-semantic relationships between objects within a scene. To address this issue, we use a method that guides Vision-Language Models(VLMs) to learn scene spatial layout relationships via Supervised Fine-Tuning (SFT). First, based on the InteriorGS dataset, we construct a Visual Question Answering (VQA) dataset spanning 100 scenes, comprising RGB images and grounding images with 2D bounding box prompts. Within this dataset, we systematically annotate the spatial-semantic relationship graphs among visible instances. Second, we utilize the parameter-efficient fine-tuning strategy of Low-Rank Adaptation (LoRA) to enhance the spatial relationship reasoning capabilities of the baseline model, Qwen2.5-VL-7B-Instruct. Furthermore, we design an easy-to-hard, three-stage curriculum learning scheme: progressing from single-image single-instance relationship reasoning, advancing to single-image multi-instance relationship understanding, and ultimately achieving global spatial layout perception across continuous frames. Comprehensive evaluations demonstrate that our method enables the model to effectively comprehend and output spatial-semantic relationship triplets in a predefined format, significantly outperforming the baseline model in inter-instance spatial-semantic reasoning. Our research validates the feasibility of endowing existing VLMs with preliminary spatial intelligence via SFT, laying the foundation for constructing next-generation semantic scene representations enriched with spatial relational information.