N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models

• URL: http://arxiv.org/abs/2512.16561v1
• Date: Thu, 18 Dec 2025 14:03:44 GMT
• Title: N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models
• Authors: Yuxin Wang, Lei Ke, Boqiang Zhang, Tianyuan Qu, Hanxun Yu, Zhenpeng Huang, Meng Yu, Dan Xu, Dong Yu,
• Abstract summary: N3D-VLM is a novel unified framework that seamlessly integrates native 3D object perception with 3D-aware visual reasoning.<n>Unlike conventional end-to-end models that directly predict answers from RGB/RGB-D inputs, our approach equips the model with native 3D object perception capabilities.
• Score: 45.008146973701855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While current multimodal models can answer questions based on 2D images, they lack intrinsic 3D object perception, limiting their ability to comprehend spatial relationships and depth cues in 3D scenes. In this work, we propose N3D-VLM, a novel unified framework that seamlessly integrates native 3D object perception with 3D-aware visual reasoning, enabling both precise 3D grounding and interpretable spatial understanding. Unlike conventional end-to-end models that directly predict answers from RGB/RGB-D inputs, our approach equips the model with native 3D object perception capabilities, enabling it to directly localize objects in 3D space based on textual descriptions. Building upon accurate 3D object localization, the model further performs explicit reasoning in 3D, achieving more interpretable and structured spatial understanding. To support robust training for these capabilities, we develop a scalable data construction pipeline that leverages depth estimation to lift large-scale 2D annotations into 3D space, significantly increasing the diversity and coverage for 3D object grounding data, yielding over six times larger than the largest existing single-image 3D detection dataset. Moreover, the pipeline generates spatial question-answering datasets that target chain-of-thought (CoT) reasoning in 3D, facilitating joint training for both 3D object localization and 3D spatial reasoning. Experimental results demonstrate that our unified framework not only achieves state-of-the-art performance on 3D grounding tasks, but also consistently surpasses existing methods in 3D spatial reasoning in vision-language model.

Related papers

• 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)
• 3D Aware Region Prompted Vision Language Model [99.4106711584584]
  SR-3D connects single-view 2D images and multi-view 3D data through a shared visual token space.<n> SR-3D supports flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D.
  arXiv  Detail & Related papers  (2025-09-16T17:59:06Z)
• Spatial 3D-LLM: Exploring Spatial Awareness in 3D Vision-Language Models [12.545622346725544]
  New era has unlocked exciting possibilities for extending Large Language Models (LLMs) to tackle 3D vision-language tasks.<n>We propose Spatial 3D-LLM, a 3D MLLM specifically designed to enhance spatial awareness for 3D vision-language tasks.<n>We introduce two novel tasks: 3D object distance measurement and 3D layout editing, and construct a 3D instruction dataset, MODEL, to evaluate the model's spatial awareness capabilities.
  arXiv  Detail & Related papers  (2025-07-22T12:32:35Z)
• 3DGeoDet: General-purpose Geometry-aware Image-based 3D Object Detection [17.502554516157893]
  3DGeoDet is a novel geometry-aware 3D object detection approach.<n>It effectively handles single- and multi-view RGB images in indoor and outdoor environments.
  arXiv  Detail & Related papers  (2025-06-11T09:18:36Z)
• Unifying 2D and 3D Vision-Language Understanding [85.84054120018625]
  We introduce UniVLG, a unified architecture for 2D and 3D vision-language learning.<n>UniVLG bridges the gap between existing 2D-centric models and the rich 3D sensory data available in embodied systems.
  arXiv  Detail & Related papers  (2025-03-13T17:56:22Z)
• 3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark [25.311698492216127]
  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)
• 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)
• OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation [107.71752592196138]
  We propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets. Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos.
  arXiv  Detail & Related papers  (2023-01-18T18:14:18Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)

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.