Embodied Intelligence for 3D Understanding: A Survey on 3D Scene Question Answering

• URL: http://arxiv.org/abs/2502.00342v1
• Date: Sat, 01 Feb 2025 07:01:33 GMT
• Title: Embodied Intelligence for 3D Understanding: A Survey on 3D Scene Question Answering
• Authors: Zechuan Li, Hongshan Yu, Yihao Ding, Yan Li, Yong He, Naveed Akhtar,
• Abstract summary: 3D Scene Question Answering represents an interdisciplinary task that integrates 3D visual perception and natural language processing. Recent advances in large multimodal modelling have driven the creation of diverse datasets and spurred the development of instruction-tuning and zero-shot methods for 3D SQA. This paper presents the first comprehensive survey of 3D SQA, systematically reviewing datasets, methodologies, and evaluation metrics.
• Score: 28.717312557697376
• License:
• Abstract: 3D Scene Question Answering (3D SQA) represents an interdisciplinary task that integrates 3D visual perception and natural language processing, empowering intelligent agents to comprehend and interact with complex 3D environments. Recent advances in large multimodal modelling have driven the creation of diverse datasets and spurred the development of instruction-tuning and zero-shot methods for 3D SQA. However, this rapid progress introduces challenges, particularly in achieving unified analysis and comparison across datasets and baselines. This paper presents the first comprehensive survey of 3D SQA, systematically reviewing datasets, methodologies, and evaluation metrics while highlighting critical challenges and future opportunities in dataset standardization, multimodal fusion, and task design.

Related papers

• Multi-modal Situated Reasoning in 3D Scenes [32.800524889357305]
  We propose Multi-modal Situated Question Answering (MSQA), a large-scale multi-modal situated reasoning dataset. MSQA includes 251K situated question-answering pairs across 9 distinct question categories, covering complex scenarios within 3D scenes. We also devise the Multi-modal Situated Next-step Navigation (MSNN) benchmark to evaluate models' situated reasoning for navigation.
  arXiv  Detail & Related papers  (2024-09-04T02:37:38Z)
• 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)
• 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)
• 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. 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)
• 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)
• A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes [80.20670062509723]
  3D dense captioning is an emerging vision-language bridging task that aims to generate detailed descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field.
  arXiv  Detail & Related papers  (2024-03-12T10:04:08Z)
• Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation [28.417029383793068]
  Multi-modal 3D scene understanding has gained considerable attention due to its wide applications in many areas, such as autonomous driving and human-computer interaction. introducing an additional modality not only elevates the richness and precision of scene interpretation but also ensures a more robust and resilient understanding. We present a novel taxonomy that delivers a thorough categorization of existing methods according to modalities and tasks, exploring their respective strengths and limitations.
  arXiv  Detail & Related papers  (2023-10-24T09:39:05Z)
• Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes [68.61199623705096]
  Training models to apply common-sense linguistic knowledge and visual concepts from 2D images to 3D scene understanding is a promising direction that researchers have only recently started to explore. We propose a novel 3D pre-training Vision-Language method, namely Multi-CLIP, that enables a model to learn language-grounded and transferable 3D scene point cloud representations.
  arXiv  Detail & Related papers  (2023-06-04T11:08:53Z)
• Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline [35.717047755880536]
  3D visual question answering (VQA) task is less exploited and is more susceptible to language priors and co-reference ambiguity. We collect a new 3D VQA dataset with diverse and relatively free-form question-answer pairs, as well as dense and completely grounded bounding box annotations. We propose a new 3D VQA framework to effectively predict the completely visually grounded and explainable answer.
  arXiv  Detail & Related papers  (2022-09-24T15:09:02Z)

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.