Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM

• URL: http://arxiv.org/abs/2607.05493v1
• Date: Mon, 06 Jul 2026 18:00:00 GMT
• Title: Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM
• Abstract summary: Natural-language queries about 3D environments become actionable when responses are verifiable and metric.<n>Existing 3D large multimodal models (LMMs) approaches remain limited.<n>We present Ground3D-LMM, a unified model that takes a point cloud and an optional RGB image as input and supports 3D spatial conversation.
• Score: 54.042182747931925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural-language queries about 3D environments become actionable when responses are verifiable and metric. Verifiability requires explicit grounding to the referred 3D region, while metric answers report physical measurements in real-world units (e.g., size, thickness, clearance, and distance). Existing 3D large multimodal models (LMMs) approaches remain limited: conversational systems typically respond without explicit 3D grounding, while 3D grounding models are not designed for interactive, metric-aware dialogue. In this paper, we present Ground3D-LMM, a unified model that takes a point cloud and an optional RGB image as input and supports 3D spatial conversation with (i) point-grounded responses and (ii) metric numeric outputs at both object and part granularity, including multi-object queries. To evaluate this intersection of grounding and measurement, we define the 3D Grounded Measurement task, which requires predicting the referred 3D region and the corresponding metric quantities in real-world units. We introduce a large-scale dataset built on ScanNet and ScanNet++ datasets with dense object and part annotations and roughly 2.5M question-answer pairs spanning eight tasks, along with a manually verified test set. Extensive experiments on multiple datasets and tasks show that our proposed Ground3D-LMM model provides a strong baseline for grounded, metric-aware 3D conversational understanding. Our dataset and model are publicly available.

Related papers

• Map-Det3D: Metric Feed-Forward 3D Reconstruction Prior for Multi-view 3D Object Detection from Streaming Inputs [62.865932175477035]
  Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors.<n>We propose Map-Det3D, an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB.
  arXiv  Detail & Related papers  (2026-08-12T15:33:42Z)
• N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models [45.008146973701855]
  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.
  arXiv  Detail & Related papers  (2025-12-18T14:03:44Z)
• MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs [19.70116190496693]
  Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space.<n>In this work, we leverage large-scale high-quality 3D scene data with open-set annotations to introduce 1) a novel supervised fine-tuning dataset and 2) a new evaluation benchmark, focused on indoor scenes.
  arXiv  Detail & Related papers  (2025-03-17T12:34:22Z)
• Space3D-Bench: Spatial 3D Question Answering Benchmark [49.259397521459114]
  We present Space3D-Bench - a collection of 1000 general spatial questions and answers related to scenes of the Replica dataset. We provide an assessment system that grades natural language responses based on predefined ground-truth answers. Finally, we introduce a baseline called RAG3D-Chat integrating the world understanding of foundation models with rich context retrieval.
  arXiv  Detail & Related papers  (2024-08-29T16:05:22Z)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.<n>The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• Grounded 3D-LLM with Referent Tokens [58.890058568493096]
  We propose Grounded 3D-LLM to consolidate various 3D vision tasks within a unified generative framework. The model uses scene referent tokens as special noun phrases to reference 3D scenes. Per-task instruction-following templates are employed to ensure natural and diversity in translating 3D vision tasks into language formats.
  arXiv  Detail & Related papers  (2024-05-16T18:03:41Z)
• VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
  monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
  arXiv  Detail & Related papers  (2024-04-15T03:12:12Z)

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.