Multi-modal Situated Reasoning in 3D Scenes

• URL: http://arxiv.org/abs/2409.02389v1
• Date: Wed, 4 Sep 2024 02:37:38 GMT
• Title: Multi-modal Situated Reasoning in 3D Scenes
• Authors: Xiongkun Linghu, Jiangyong Huang, Xuesong Niu, Xiaojian Ma, Baoxiong Jia, Siyuan Huang,
• Abstract summary: 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.
• Score: 32.800524889357305
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Situation awareness is essential for understanding and reasoning about 3D scenes in embodied AI agents. However, existing datasets and benchmarks for situated understanding are limited in data modality, diversity, scale, and task scope. To address these limitations, we propose Multi-modal Situated Question Answering (MSQA), a large-scale multi-modal situated reasoning dataset, scalably collected leveraging 3D scene graphs and vision-language models (VLMs) across a diverse range of real-world 3D scenes. MSQA includes 251K situated question-answering pairs across 9 distinct question categories, covering complex scenarios within 3D scenes. We introduce a novel interleaved multi-modal input setting in our benchmark to provide text, image, and point cloud for situation and question description, resolving ambiguity in previous single-modality convention (e.g., text). Additionally, we devise the Multi-modal Situated Next-step Navigation (MSNN) benchmark to evaluate models' situated reasoning for navigation. Comprehensive evaluations on MSQA and MSNN highlight the limitations of existing vision-language models and underscore the importance of handling multi-modal interleaved inputs and situation modeling. Experiments on data scaling and cross-domain transfer further demonstrate the efficacy of leveraging MSQA as a pre-training dataset for developing more powerful situated reasoning models.

Related papers

• Multi-modal Relation Distillation for Unified 3D Representation Learning [30.942281325891226]
  Multi-modal Relation Distillation (MRD) is a tri-modal pre-training framework designed to distill reputable large Vision-Language Models (VLM) into 3D backbones. MRD aims to capture both intra-relations within each modality as well as cross-relations between different modalities and produce more discriminative 3D shape representations.
  arXiv  Detail & Related papers  (2024-07-19T03:43:48Z)
• 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)
• OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning [68.45848423501927]
  We propose a holistic framework for strong alignment between agent models and 3D driving tasks. Our framework starts with a novel 3D MLLM architecture that uses sparse queries to lift and compress visual representations into 3D. We propose OmniDrive-nuScenes, a new visual question-answering dataset challenging the true 3D situational awareness of a model.
  arXiv  Detail & Related papers  (2024-05-02T17:59:24Z)
• 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)
• Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning [40.972648044298374]
  Multi-Modal Large Language Models (MLLMs) have demonstrated impressive performance in various VQA tasks. They often lack interpretability and struggle with complex visual inputs. We introduce the large-scale Visual CoT dataset comprising 438k question-answer pairs. We propose a multi-turn processing pipeline that dynamically focuses on visual inputs and provides interpretable thoughts.
  arXiv  Detail & Related papers  (2024-03-25T17:59:23Z)
• X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning [109.9413329636322]
  This paper introduces an efficient framework that integrates multiple modalities (images, 3D, audio and video) to a frozen Large Language Models (LLMs) Our approach explores two distinct projection mechanisms: Q-Formers and Linear Projections (LPs)
  arXiv  Detail & Related papers  (2023-11-30T18:43:51Z)
• ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos [53.92440577914417]
  ACQUIRED consists of 3.9K annotated videos, encompassing a wide range of event types and incorporating both first and third-person viewpoints. Each video is annotated with questions that span three distinct dimensions of reasoning, including physical, social, and temporal. We benchmark our dataset against several state-of-the-art language-only and multimodal models and experimental results demonstrate a significant performance gap.
  arXiv  Detail & Related papers  (2023-11-02T22:17:03Z)
• 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)
• MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes [62.20046129613934]
  We propose a novel multi-view fusion framework, namely multi-view MRD network (MMRDN) We project the 2D data from different views into a common hidden space and fit the embeddings with a set of Von-Mises-Fisher distributions. We select a set of $K$ Maximum Vertical Neighbors (KMVN) points from the point cloud of each object pair, which encodes the relative position of these two objects.
  arXiv  Detail & Related papers  (2023-04-25T05:55:29Z)
• Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents [49.904531485843464]
  In this paper, we discuss the main challenge: insufficient, or even no, labeled data for real-world indoor environments. We describe MMISM (Multi-modality input Multi-task output Indoor Scene understanding Model) to tackle the above challenges. MMISM considers RGB images as well as sparse Lidar points as inputs and 3D object detection, depth completion, human pose estimation, and semantic segmentation as output tasks. We show that MMISM performs on par or even better than single-task models.
  arXiv  Detail & Related papers  (2022-09-27T04:49:19Z)

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.