Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation

• URL: http://arxiv.org/abs/2506.23120v1
• Date: Sun, 29 Jun 2025 06:58:08 GMT
• Title: Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation
• Abstract summary: We introduce Relevant Reasoning (R$2$S), a reasoning-based segmentation framework.<n>We also introduce 3D ReasonSeg, a reasoning-based segmentation dataset.<n>Both experiments demonstrate that the R$2$S and 3D ReasonSeg effectively endow 3D point cloud perception with stronger spatial reasoning capabilities.
• Score: 50.81551581148339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in point cloud perception have demonstrated remarkable progress in scene understanding through vision-language alignment leveraging large language models (LLMs). However, existing methods may still encounter challenges in handling complex instructions that require accurate spatial reasoning, even if the 3D point cloud data provides detailed spatial cues such as size and position for identifying the targets. To tackle this issue, we propose Relevant Reasoning Segmentation (R$^2$S), a reasoning-based segmentation framework. The framework emulates human cognitive processes by decomposing spatial reasoning into two sequential stages: first identifying relevant elements, then processing instructions guided by their associated visual priors. Furthermore, acknowledging the inadequacy of existing datasets in complex reasoning tasks, we introduce 3D ReasonSeg, a reasoning-based segmentation dataset comprising 25,185 training samples and 3,966 validation samples with precise annotations. Both quantitative and qualitative experiments demonstrate that the R$^2$S and 3D ReasonSeg effectively endow 3D point cloud perception with stronger spatial reasoning capabilities, and we hope that they can serve as a new baseline and benchmark for future work.

Related papers

• SpaceEra++: A Unified Framework Towards 3D Spatial Reasoning in Video [88.79625979053873]
  We propose a novel framework, SpaceEra, for pre-trained vision-language models (VLMs)<n>We extend the original framework into a comprehensive system, termed SpaceEra++, which spans data construction, model design, training optimization, and prompting inference.<n>In addition, to enhance spatial reasoning, we develop SpaceAlign, which enforces pairwise object constraints by jointly exploiting absolute coordinates and relative spatial relations.
  arXiv  Detail & Related papers  (2026-07-02T06:56:29Z)
• Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models [50.6415287154632]
  Embodied3DBench is a robot-centric benchmark targeting low-level spatial intelligence in 3D environments.<n>The benchmark spans 12 subcategories and contains over 21k high-quality question-answer pairs.
  arXiv  Detail & Related papers  (2026-05-27T20:28:56Z)
• PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought [17.13654442098613]
  Chain-of-Thought (CoT) reasoning has shown strong effectiveness in LLMs and image-based MLLMs.<n>We propose a data-centric framework for constructing large-scale CoT supervision tailored to 3D point cloud understanding.<n>Fine-tuning PointLLM on PoCoTI yields PointLLM-R, a reasoning-capable 3D multimodal language model.
  arXiv  Detail & Related papers  (2026-05-21T05:19:51Z)
• Actial: Activate Spatial Reasoning Ability of Multimodal Large Language Models [75.45940282834327]
  We introduce Viewpoint Learning, a task designed to evaluate and improve the spatial reasoning capabilities of MLLMs.<n>We present the Viewpoint-100K dataset, consisting of 100K object-centric image pairs with diverse viewpoints and corresponding question-answer pairs.<n>Our approach employs a two-stage fine-tuning strategy, resulting in significant improvements across multiple tasks.
  arXiv  Detail & Related papers  (2025-11-03T14:27:00Z)
• SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models [73.19077622773075]
  We present a comprehensive methodology for building spatial intelligence progressively.<n>We introduce SpatialLadder-26k, a multimodal dataset containing 26,610 samples spanning object localization, single image, multi-view, and video spatial reasoning tasks.<n>We design a three-stage progressive training framework that establishes spatial perception through object localization, develops spatial understanding through multi-dimensional spatial tasks, and strengthens complex reasoning via reinforcement learning with verifiable rewards.
  arXiv  Detail & Related papers  (2025-10-09T17:50:54Z)
• Enhancing Spatial Reasoning through Visual and Textual Thinking [45.0026939683271]
  The spatial reasoning task aims to reason about the spatial relationships in 2D and 3D space.<n>Although vision language models (VLMs) have developed rapidly in recent years, they are still struggling with the spatial reasoning task.<n>We introduce a method that can enhance spatial reasoning through Visual and Textual thinking Simultaneously.
  arXiv  Detail & Related papers  (2025-07-28T05:24:54Z)
• Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models [14.442394137843923]
  We present a detailed analysis that first delineates the core elements of spatial reasoning.<n>We then assesses the performance of these models in both synthetic and real-world images.
  arXiv  Detail & Related papers  (2025-03-25T14:34:06Z)
• 3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset [13.808860456901204]
  We introduce a scalable 3D benchmark, accompanied by a large-scale instruction-tuning dataset known as 3DBench. Specifically, we establish the benchmark that spans a wide range of spatial and semantic scales, from object-level to scene-level. We present a rigorous pipeline for automatically constructing scalable 3D instruction-tuning datasets, covering 10 diverse multi-modal tasks with more than 0.23 million QA pairs generated in total.
  arXiv  Detail & Related papers  (2024-04-23T02:06:10Z)
• PointLLM: Empowering Large Language Models to Understand Point Clouds [63.39876878899682]
  PointLLM understands colored object point clouds with human instructions. It generates contextually appropriate responses, illustrating its grasp of point clouds and common sense.
  arXiv  Detail & Related papers  (2023-08-31T17:59:46Z)
• Explore In-Context Learning for 3D Point Cloud Understanding [71.20912026561484]
  We introduce a novel framework, named Point-In-Context, designed especially for in-context learning in 3D point clouds. We propose the Joint Sampling module, carefully designed to work in tandem with the general point sampling operator. We conduct extensive experiments to validate the versatility and adaptability of our proposed methods in handling a wide range of tasks.
  arXiv  Detail & Related papers  (2023-06-14T17:53:21Z)
• Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition [57.08108545219043]
  Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches. This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
  arXiv  Detail & Related papers  (2023-05-12T00:13:17Z)
• ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection [114.54835359657707]
  ProposalContrast is an unsupervised point cloud pre-training framework. It learns robust 3D representations by contrasting region proposals. ProposalContrast is verified on various 3D detectors.
  arXiv  Detail & Related papers  (2022-07-26T04:45:49Z)
• Salient Object Detection for Point Clouds [13.852801615283747]
  We present a novel view-dependent perspective of salient objects, reasonably reflecting the most eye-catching objects in point cloud scenarios. We introduce PCSOD, the first dataset proposed for point cloud SOD consisting of 2,872 in-/out-door 3D views. The proposed model can effectively analyze irregular and unordered points for detecting salient objects.
  arXiv  Detail & Related papers  (2022-07-25T03:35:46Z)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion [38.05362492645094]
  Real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. We concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. By comparing with state-of-the-art networks on three different benchmarks, we demonstrate the effectiveness of our network.
  arXiv  Detail & Related papers  (2021-03-12T04:13:20Z)

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.