Spatial Understanding from Videos: Structured Prompts Meet Simulation Data

• URL: http://arxiv.org/abs/2506.03642v2
• Date: Fri, 19 Sep 2025 05:48:14 GMT
• Title: Spatial Understanding from Videos: Structured Prompts Meet Simulation Data
• Authors: Haoyu Zhang, Meng Liu, Zaijing Li, Haokun Wen, Weili Guan, Yaowei Wang, Liqiang Nie,
• Abstract summary: We present a unified framework for enhancing 3D spatial reasoning in pre-trained vision-language models without modifying their architecture.<n>This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes.
• Score: 89.77871049500546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual-spatial understanding, the ability to infer object relationships and layouts from visual input, is fundamental to downstream tasks such as robotic navigation and embodied interaction. However, existing methods face spatial uncertainty and data scarcity, limiting the 3D spatial reasoning capability of pre-trained vision-language models (VLMs). To address these challenges, we present a unified framework for enhancing 3D spatial reasoning in pre-trained VLMs without modifying their architecture. This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes through an automated construction process designed for fine-tuning. Extensive experiments across multiple benchmarks demonstrate the individual and combined effectiveness of our prompting and fine-tuning strategies, and yield insights that may inspire future research on visual-spatial understanding.

Related papers

• SpatialMosaic: A Multiview VLM Dataset for Partial Visibility [25.874299974251965]
  We propose a scalable multi-view data generation and annotation pipeline that constructs realistic spatial reasoning QAs.<n>We introduce SpatialMosaic-Bench, a benchmark for evaluating multi-view spatial reasoning under realistic and challenging scenarios.<n>We also present SpatialMosaicVLM, a hybrid framework that integrates 3D reconstruction models as geometry encoders within Vision-Language Models.
  arXiv  Detail & Related papers  (2025-12-29T10:48:54Z)
• Lemon: A Unified and Scalable 3D Multimodal Model for Universal Spatial Understanding [80.66591664266744]
  Lemon is a unified transformer architecture that processes 3D point cloud patches and language tokens as a single sequence.<n>To handle the complexity of 3D data, we develop a structured patchification and tokenization scheme that preserves spatial context.<n>Lemon establishes new state-of-the-art performance across comprehensive 3D understanding and reasoning tasks.
  arXiv  Detail & Related papers  (2025-12-14T20:02:43Z)
• Abstract 3D Perception for Spatial Intelligence in Vision-Language Models [100.13033631690114]
  Vision-language models (VLMs) struggle with 3D-related tasks such as spatial cognition and physical understanding.<n>We introduce SandboxVLM, a framework that leverages abstract bounding boxes to encode geometric structure and physical kinematics for VLM.<n>Our approach consistently improves spatial intelligence, achieving an 8.3% gain on SAT Real compared with baseline methods.
  arXiv  Detail & Related papers  (2025-11-14T04:16:09Z)
• SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding [64.86119288520419]
  multimodal language models struggle with spatial reasoning across time and space.<n>We present SIMS-V -- a systematic data-generation framework that leverages the privileged information of 3D simulators.<n>Our approach demonstrates robust generalization, maintaining performance on general video understanding while showing substantial improvements on embodied and real-world spatial tasks.
  arXiv  Detail & Related papers  (2025-11-06T18:53:31Z)
• Queryable 3D Scene Representation: A Multi-Modal Framework for Semantic Reasoning and Robotic Task Planning [28.803789915686398]
  3D Queryable Scene Representation (3D QSR) is a framework built on multimedia data that unifies three complementary 3D representations.<n>Built on an object-centric design, the framework integrates with large vision-language models to enable semantic queryability.<n>Results demonstrate the framework's ability to facilitate scene understanding and integrate spatial and semantic reasoning.
  arXiv  Detail & Related papers  (2025-09-24T12:53:32Z)
• Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT [10.984522161856955]
  3D reconstruction is a cornerstone technology for numerous applications, including augmented/virtual reality, autonomous driving, and robotics.<n>Deep learning has catalyzed a paradigm shift in 3D reconstruction.<n>New models employ a unified deep network to jointly infer camera poses and dense geometry directly from an Unconstrained set of images in a single forward pass.
  arXiv  Detail & Related papers  (2025-07-11T09:41:54Z)
• VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction [86.82819259860186]
  We introduce VLM-3R, a unified framework for Vision-Language Models (VLMs) that incorporates 3D Reconstructive instruction tuning.<n>VLM-3R processes monocular video frames by employing a geometry encoder to derive implicit 3D tokens that represent spatial understanding.
  arXiv  Detail & Related papers  (2025-05-26T17:56:30Z)
• Agentic 3D Scene Generation with Spatially Contextualized VLMs [67.31920821192323]
  We introduce a new paradigm that enables vision-language models to generate, understand, and edit complex 3D environments.<n>We develop an agentic 3D scene generation pipeline in which the VLM iteratively reads from and updates the spatial context.<n>Results show that our framework can handle diverse and challenging inputs, achieving a level of generalization not observed in prior work.
  arXiv  Detail & Related papers  (2025-05-26T15:28:17Z)
• How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM [39.65493154187172]
  Large Language Models (LLMs) have been leveraged to enhance 3D understanding tasks, showing potential to surpass traditional computer vision methods.<n>We propose a taxonomy that categorizes existing methods into three branches: image-based methods deriving 3D understanding from 2D visual data, point cloud-based methods working directly with 3D representations, and hybrid modality-based methods combining multiple data streams.
  arXiv  Detail & Related papers  (2025-04-08T08:11:39Z)
• LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models [57.92316645992816]
  Spatial reasoning is a fundamental aspect of human cognition, enabling intuitive understanding and manipulation of objects in three-dimensional space.<n>We introduce LayoutVLM, a framework and scene layout representation that exploits the semantic knowledge of Vision-Language Models (VLMs)<n>We demonstrate that fine-tuning VLMs with the proposed scene layout representation extracted from existing scene datasets can improve their reasoning performance.
  arXiv  Detail & Related papers  (2024-12-03T06:15:04Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors [42.85605789984155]
  Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA) We present SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner. Our spatial reasoning-imbued VLM performs well on various forms of spatial VQA and can extend to help in various downstream robotics tasks such as pick and stack and trajectory planning.
  arXiv  Detail & Related papers  (2024-03-18T17:38:29Z)
• Benchmarking Unsupervised Object Representations for Video Sequences [111.81492107649889]
  We compare the perceptual abilities of four object-centric approaches: ViMON, OP3, TBA and SCALOR. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking. Our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
  arXiv  Detail & Related papers  (2020-06-12T09:37:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.