SpatialReasoner: Towards Explicit and Generalizable 3D Spatial Reasoning

• URL: http://arxiv.org/abs/2504.20024v1
• Date: Mon, 28 Apr 2025 17:48:43 GMT
• Title: SpatialReasoner: Towards Explicit and Generalizable 3D Spatial Reasoning
• Authors: Wufei Ma, Yu-Cheng Chou, Qihao Liu, Xingrui Wang, Celso de Melo, Jieneng Chen, Jianwen Xie, Alan Yuille,
• Abstract summary: We introduce a novel large vision-language model (LVLM) that address 3D spatial reasoning with explicit 3D representations shared between stages.<n>Explicit 3D representations provide a coherent interface that supports advanced 3D spatial reasoning.
• Score: 25.006301640162846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies in 3D spatial reasoning explore data-driven approaches and achieve enhanced spatial reasoning performance with reinforcement learning (RL). However, these methods typically perform spatial reasoning in an implicit manner, and it remains underexplored whether the acquired 3D knowledge generalizes to unseen question types at any stage of the training. In this work we introduce SpatialReasoner, a novel large vision-language model (LVLM) that address 3D spatial reasoning with explicit 3D representations shared between stages -- 3D perception, computation, and reasoning. Explicit 3D representations provide a coherent interface that supports advanced 3D spatial reasoning and enable us to study the factual errors made by LVLMs. Results show that our SpatialReasoner achieve improved performance on a variety of spatial reasoning benchmarks and generalizes better when evaluating on novel 3D spatial reasoning questions. Our study bridges the 3D parsing capabilities of prior visual foundation models with the powerful reasoning abilities of large language models, opening new directions for 3D spatial reasoning.

Related papers

• The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? [42.3970767778131]
  3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention.<n>Despite some promising results, the role of point clouds in 3D spatial reasoning remains under-explored.<n>We comprehensively evaluate and analyze these models to answer the research question: textitDoes point cloud truly boost the spatial reasoning capacities of 3D LLMs?
  arXiv  Detail & Related papers  (2025-04-06T16:38:48Z)
• MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation [87.30919771444117]
  Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning. Recent multimodal large language models (MLLMs) have demonstrated impressive 2D image reasoning segmentation. We introduce MLLM-For3D, a framework that transfers knowledge from 2D MLLMs to 3D scene understanding.
  arXiv  Detail & Related papers  (2025-03-23T16:40:20Z)
• 3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark [17.94511890272007]
  3D spatial reasoning is the ability to analyze and interpret the positions, orientations, and spatial relationships of objects within the 3D space.<n>Large multi-modal models (LMMs) have achieved remarkable progress in a wide range of image and video understanding tasks.<n>We present the first comprehensive 3D spatial reasoning benchmark, 3DSRBench, with 2,772 manually annotated visual question-answer pairs.
  arXiv  Detail & Related papers  (2024-12-10T18:55:23Z)
• GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding [53.42728468191711]
  Open-Vocabulary 3D object affordance grounding aims to anticipate action possibilities'' regions on 3D objects with arbitrary instructions. We propose GREAT (GeometRy-intEntion collAboraTive inference) for Open-Vocabulary 3D Object Affordance Grounding.
  arXiv  Detail & Related papers  (2024-11-29T11:23:15Z)
• Multimodal 3D Reasoning Segmentation with Complex Scenes [92.92045550692765]
  We bridge the research gaps by proposing a 3D reasoning segmentation task for multiple objects in scenes.<n>We create ReasonSeg3D, a benchmark that integrates 3D segmentation masks and 3D spatial relations with generated question-answer pairs.<n>In addition, we design MORE3D, a novel 3D reasoning network that works with queries of multiple objects.
  arXiv  Detail & Related papers  (2024-11-21T08:22:45Z)
• SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models [45.28780381341979]
  We introduce a scalable situated 3D dataset, named Spartun3D, that incorporates various situated spatial reasoning tasks.<n>We also propose Spartun3D-LLM, built on an existing 3D-based LLM but integrated with a novel situated spatial alignment module.
  arXiv  Detail & Related papers  (2024-10-04T19:22:20Z)
• LLMI3D: MLLM-based 3D Perception from a Single 2D Image [77.13869413871028]
  multimodal large language models (MLLMs) excel in general capacity but underperform in 3D tasks.<n>In this paper, we propose solutions for weak 3D local spatial object perception, poor text-based geometric numerical output, and inability to handle camera focal variations.<n>We employ parameter-efficient fine-tuning for a pre-trained MLLM and develop LLMI3D, a powerful 3D perception MLLM.
  arXiv  Detail & Related papers  (2024-08-14T10:00:16Z)
• ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities [23.18281583681258]
  We propose a new task called 3D reasoning grounding and introduce a new benchmark ScanReason. ScanReason provides over 10K question-answer-location pairs from five reasoning types that require the synerization of reasoning and grounding. A chain-of-grounding mechanism is proposed to further boost the performance with interleaved reasoning and grounding steps during inference.
  arXiv  Detail & Related papers  (2024-07-01T17:59:35Z)
• Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model [108.35777542298224]
  Reason3D processes point cloud data and text prompts to produce textual responses and segmentation masks.<n>We propose a hierarchical mask decoder that employs a coarse-to-fine approach to segment objects within expansive scenes.
  arXiv  Detail & Related papers  (2024-05-27T17:59:41Z)
• When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models [113.18524940863841]
  This survey provides a comprehensive overview of the methodologies enabling large language models to process, understand, and generate 3D data. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs) It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue.
  arXiv  Detail & Related papers  (2024-05-16T16:59:58Z)
• 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)
• Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes [56.727745047799246]
  3D scene understanding has gained significant attention due to its wide range of applications. This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs.
  arXiv  Detail & Related papers  (2023-08-17T03:52:15Z)

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.