MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations

Ruiyuan Lyu, Tai Wang, Jingli Lin, Shuai Yang, Xiaohan Mao, Yilun Chen, Runsen Xu, Haifeng Huang, Chenming Zhu, Dahua Lin, Jiangmiao Pang·June 13, 2024

Summary

The paper introduces MMScan, a groundbreaking multi-modal 3D scene dataset with 6.9 million hierarchical language annotations, designed to enhance 3D scene understanding. It covers object, region, and inter-object relationships, using a top-down approach and incorporating VLMs, human correction, and existing scanning data. MMScan consists of 1.4M captions, 7.7k regions, and 3.04M samples for tasks like visual grounding and question-answering. The dataset addresses limitations of previous works by providing comprehensive spatial and attribute annotations in 5,000 real-scanned scenes. Experiments show improved performance in state-of-the-art models, with a focus on benchmarking and evaluating the ability to understand complex prompts and spatial relationships. MMScan is made available for research, aiming to advance 3D perception in embodied agents and LLMs.

Key findings

18

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of language-grounded 3D scene understanding by introducing multi-modal 3D perception benchmarks for visual grounding and question-answering . This problem is not entirely new, as there have been previous efforts in creating 3D scene datasets with multi-modal annotations focusing on language-grounded understanding, such as ScanRefer, ReferIt3D, and ScanQA . However, the paper contributes by expanding the scope and complexity of the datasets to enhance the capabilities of models in understanding comprehensive spatial and attribute information in complex prompts .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to the effectiveness and performance of multi-modal 3D perception benchmarks, specifically focusing on visual grounding and question-answering tasks . The study aims to evaluate the capabilities of models in understanding complex prompts that involve comprehensive spatial and attribute understanding, highlighting the challenges in this domain . Additionally, the paper explores the improvement opportunities by including the image modality to enhance semantic understanding and enhancing the selection of candidate objects for better model performance .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models in the field of multi-modal 3D scene understanding and language-grounded annotations . One key contribution is the establishment of two multi-modal 3D perception benchmarks: visual grounding and question-answering. These benchmarks are generated based on meta-annotations, incorporating single target and inter-target relationships with 5 sub-classes to evaluate models comprehensively . The paper introduces a valuable resource for training 3D grounding and large language models by seamlessly integrating meta-annotations into scene-level captions .

In terms of methodology, the paper evaluates representative baselines on the established benchmarks and discusses emerging challenges in visual grounding and question-answering tasks. It highlights the difficulty of understanding complex prompts that require comprehensive spatial and attribute understanding, suggesting the inclusion of the image modality to enhance semantic understanding and improve candidate object selection as promising directions . The study also addresses the unsatisfactory performance of current 3D-LLMs on the question-answering benchmark and demonstrates a significant improvement in accuracy, up to 25.6%, through data-driven instruction tuning .

Furthermore, the paper leverages MMScan's captions to train grounding and 3D-LLM models, leading to a 7.17% increase in Average Precision (AP) and state-of-the-art performance on existing visual grounding and question-answering benchmarks. This approach enables enhanced instruction following performance in real-world scenarios . The study emphasizes the importance of scaling up 3D-text data pairs to advance multi-modal 3D learning. Initiatives like 3D-VisTA and EmbodiedScan aim to generate scene descriptions, collect more scenes, and annotate additional objects to scale up annotations to millions. However, the paper notes that previous works lack explicit hierarchical information in 3D scenes, such as different granularities of grounding entities, and highlights the need for human involvement in annotation scaling to serve as effective benchmarks for 3D-LLMs . The paper introduces innovative characteristics and advantages compared to previous methods in the field of multi-modal 3D scene understanding and language-grounded annotations . One key characteristic is the establishment of two multi-modal 3D perception benchmarks: visual grounding and question-answering, which incorporate single target and inter-target relationships with 5 sub-classes for comprehensive model evaluation . These benchmarks provide a significant advantage by offering a large number of samples, 1.28M and 1.76M on each benchmark, respectively, to assess model capabilities from various aspects .

In terms of advantages over previous methods, the paper addresses the limitations of existing benchmarks by highlighting the lower performance of visual grounding models and unsatisfactory results of current 3D-LLMs on their benchmarks . By leveraging meta-annotations and data-driven instruction tuning, the paper achieves a significant improvement in accuracy, up to 25.6%, on the question-answering benchmark . This improvement demonstrates the effectiveness of the proposed approach in enhancing model performance and instruction following in real-world scenarios .

Furthermore, the paper leverages MMScan's captions to train grounding and 3D-LLM models, resulting in a 7.17% increase in Average Precision (AP) and state-of-the-art performance on existing visual grounding and question-answering benchmarks . This approach not only enhances model performance but also enables better instruction following in practical applications . The integration of meta-annotations into scene-level captions provides a valuable resource for training 3D grounding and large language models, offering a comprehensive and effective approach to multi-modal 3D learning .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of multi-modal 3D scene datasets with hierarchical grounded language annotations. Noteworthy researchers in this field include the authors of the paper "MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations" . The key to the solution mentioned in the paper involves generating grounded scene-level captions from meta-annotations and integrating them to train 3D-LLMs efficiently to understand 3D scenes with hierarchical grounding capability .


How were the experiments in the paper designed?

The experiments in the paper were designed by establishing two multi-modal 3D perception benchmarks: visual grounding and question-answering. These benchmarks were created based on meta-annotations, generating samples for evaluation. The samples followed two streams: a single target and inter-target relationships, with 5 sub-classes for different aspects. This resulted in 1.28M and 1.76M samples on each benchmark, respectively, to assess the model's capabilities comprehensively .

The experiments evaluated representative baselines on the benchmarks and highlighted emerging challenges. The performance of visual grounding models was observed to be lower compared to existing benchmarks, indicating the complexity of understanding prompts entwined with spatial and attribute comprehension. Suggestions were made to enhance semantic understanding by including the image modality and improving candidate object selection. For the question-answering benchmark, current 3D-LLMs showed unsatisfactory results, with a significant 25.6% accuracy improvement achieved through data for instruction tuning .

Furthermore, the paper leveraged MMScan's captions to train grounding and 3D-LLM models, leading to a 7.17% AP increase and state-of-the-art performance on visual grounding and question-answering benchmarks. This approach notably enhanced instruction following performance in diverse scenarios .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is MMScan, a Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations . The code for the dataset is open source and can be accessed on GitHub at the following link: https://github.com/open-compass/opencompass .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper establishes two multi-modal 3D perception benchmarks focusing on visual grounding and question-answering, generating a large number of samples for evaluation . The benchmarks include single target and inter-target relationships, covering different aspects to assess the model's capabilities comprehensively .

The evaluation of representative baselines on these benchmarks revealed that the performance of visual grounding models was notably lower compared to existing benchmarks, highlighting the challenges in understanding complex prompts that involve spatial and attribute comprehension . The results also indicated the potential for enhancing semantic understanding by incorporating the image modality and improving candidate object selection .

Furthermore, the question-answering benchmark showed unsatisfactory results with current 3D-LLMs but demonstrated a significant improvement of up to 25.6% accuracy through data for instruction tuning . Leveraging the captions from MMScan for training grounding and 3D-LLM models led to a substantial increase in performance, achieving state-of-the-art results in visual grounding and question-answering benchmarks, ultimately enhancing instruction following capabilities .


What are the contributions of this paper?

The contributions of the paper include the establishment of two multi-modal 3D perception benchmarks: visual grounding and question-answering. These benchmarks are generated based on meta-annotations, incorporating single target and inter-target relationships with 5 sub-classes to evaluate the model's capabilities comprehensively . The paper provides valuable resources for training 3D grounding and large language models by seamlessly integrating retained correspondence information of meta-annotations into scene-level captions . Additionally, the paper evaluates representative baselines on the benchmarks, highlighting challenges in understanding complex prompts that require spatial and attribute comprehension, and suggests directions for improvement such as enhancing semantic understanding through the image modality and refining candidate object selection . Furthermore, the paper reports significant improvements in the performance of visual grounding and question-answering models by leveraging MMScan's captions for training, resulting in state-of-the-art performance and better instruction following capabilities in diverse scenarios .


What work can be continued in depth?

To further advance the field of multi-modal 3D scene understanding, one area that can be explored in depth is the development of datasets with explicit hierarchical information in 3D scenes, encompassing different levels of grounding entities . This would involve creating datasets that go beyond object annotations and incorporate detailed hierarchical structures within scenes, enabling more comprehensive and nuanced understanding of spatial relationships and attributes within the environment. By introducing hierarchical information, researchers can enhance the complexity and richness of the data, leading to more robust and sophisticated models for 3D scene understanding and language grounding .

Tables

7

Introduction
Background
[Multi-modal 3D scene understanding challenges]
[Importance of large-scale datasets]
Objective
[Goal of MMScan: enhance 3D scene understanding]
[Addressing limitations of existing datasets]
Dataset Overview
Data Collection
Top-Down Approach
[Scene scanning and data sources]
[Incorporation of VLMs]
Human Correction and Annotation
[Crowdsourcing for language annotations]
[Quality control measures]
Dataset Structure
[Object annotations]
[Region annotations]
[Inter-object relationships]
[Spatial and attribute annotations]
Tasks and Benchmarks
Visual Grounding
[Tasks and evaluation metrics]
Question-Answering
[Sample questions and challenges]
[Performance improvements over previous works]
Experiments and Evaluation
State-of-the-Art Model Performance
[Model benchmarking]
[Complex prompt and spatial relationship understanding]
Limitations and Advancements
[Current model capabilities]
[Future research directions]
MMScan's Impact
Advancing 3D Perception
[Embodied agents]
[Large Language Models (LLMs)]
Availability and Use
[Dataset release]
[Research community access]
Conclusion
[Significance of MMScan in the field]
[Future potential and open challenges]
Basic info
papers
computer vision and pattern recognition
robotics
artificial intelligence
Advanced features
Insights
How does MMScan address the limitations of previous 3D scene datasets?
What is the primary purpose of the MMScan dataset?
What types of annotations does MMScan provide for 3D scene understanding?
How many hierarchical language annotations are included in MMScan?

MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations

Ruiyuan Lyu, Tai Wang, Jingli Lin, Shuai Yang, Xiaohan Mao, Yilun Chen, Runsen Xu, Haifeng Huang, Chenming Zhu, Dahua Lin, Jiangmiao Pang·June 13, 2024

Summary

The paper introduces MMScan, a groundbreaking multi-modal 3D scene dataset with 6.9 million hierarchical language annotations, designed to enhance 3D scene understanding. It covers object, region, and inter-object relationships, using a top-down approach and incorporating VLMs, human correction, and existing scanning data. MMScan consists of 1.4M captions, 7.7k regions, and 3.04M samples for tasks like visual grounding and question-answering. The dataset addresses limitations of previous works by providing comprehensive spatial and attribute annotations in 5,000 real-scanned scenes. Experiments show improved performance in state-of-the-art models, with a focus on benchmarking and evaluating the ability to understand complex prompts and spatial relationships. MMScan is made available for research, aiming to advance 3D perception in embodied agents and LLMs.
Mind map
[Quality control measures]
[Crowdsourcing for language annotations]
[Incorporation of VLMs]
[Scene scanning and data sources]
[Research community access]
[Dataset release]
[Large Language Models (LLMs)]
[Embodied agents]
[Future research directions]
[Current model capabilities]
[Complex prompt and spatial relationship understanding]
[Model benchmarking]
[Performance improvements over previous works]
[Sample questions and challenges]
[Tasks and evaluation metrics]
[Spatial and attribute annotations]
[Inter-object relationships]
[Region annotations]
[Object annotations]
Human Correction and Annotation
Top-Down Approach
[Addressing limitations of existing datasets]
[Goal of MMScan: enhance 3D scene understanding]
[Importance of large-scale datasets]
[Multi-modal 3D scene understanding challenges]
[Future potential and open challenges]
[Significance of MMScan in the field]
Availability and Use
Advancing 3D Perception
Limitations and Advancements
State-of-the-Art Model Performance
Question-Answering
Visual Grounding
Dataset Structure
Data Collection
Objective
Background
Conclusion
MMScan's Impact
Experiments and Evaluation
Tasks and Benchmarks
Dataset Overview
Introduction
Outline
Introduction
Background
[Multi-modal 3D scene understanding challenges]
[Importance of large-scale datasets]
Objective
[Goal of MMScan: enhance 3D scene understanding]
[Addressing limitations of existing datasets]
Dataset Overview
Data Collection
Top-Down Approach
[Scene scanning and data sources]
[Incorporation of VLMs]
Human Correction and Annotation
[Crowdsourcing for language annotations]
[Quality control measures]
Dataset Structure
[Object annotations]
[Region annotations]
[Inter-object relationships]
[Spatial and attribute annotations]
Tasks and Benchmarks
Visual Grounding
[Tasks and evaluation metrics]
Question-Answering
[Sample questions and challenges]
[Performance improvements over previous works]
Experiments and Evaluation
State-of-the-Art Model Performance
[Model benchmarking]
[Complex prompt and spatial relationship understanding]
Limitations and Advancements
[Current model capabilities]
[Future research directions]
MMScan's Impact
Advancing 3D Perception
[Embodied agents]
[Large Language Models (LLMs)]
Availability and Use
[Dataset release]
[Research community access]
Conclusion
[Significance of MMScan in the field]
[Future potential and open challenges]
Key findings
18

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of language-grounded 3D scene understanding by introducing multi-modal 3D perception benchmarks for visual grounding and question-answering . This problem is not entirely new, as there have been previous efforts in creating 3D scene datasets with multi-modal annotations focusing on language-grounded understanding, such as ScanRefer, ReferIt3D, and ScanQA . However, the paper contributes by expanding the scope and complexity of the datasets to enhance the capabilities of models in understanding comprehensive spatial and attribute information in complex prompts .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to the effectiveness and performance of multi-modal 3D perception benchmarks, specifically focusing on visual grounding and question-answering tasks . The study aims to evaluate the capabilities of models in understanding complex prompts that involve comprehensive spatial and attribute understanding, highlighting the challenges in this domain . Additionally, the paper explores the improvement opportunities by including the image modality to enhance semantic understanding and enhancing the selection of candidate objects for better model performance .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models in the field of multi-modal 3D scene understanding and language-grounded annotations . One key contribution is the establishment of two multi-modal 3D perception benchmarks: visual grounding and question-answering. These benchmarks are generated based on meta-annotations, incorporating single target and inter-target relationships with 5 sub-classes to evaluate models comprehensively . The paper introduces a valuable resource for training 3D grounding and large language models by seamlessly integrating meta-annotations into scene-level captions .

In terms of methodology, the paper evaluates representative baselines on the established benchmarks and discusses emerging challenges in visual grounding and question-answering tasks. It highlights the difficulty of understanding complex prompts that require comprehensive spatial and attribute understanding, suggesting the inclusion of the image modality to enhance semantic understanding and improve candidate object selection as promising directions . The study also addresses the unsatisfactory performance of current 3D-LLMs on the question-answering benchmark and demonstrates a significant improvement in accuracy, up to 25.6%, through data-driven instruction tuning .

Furthermore, the paper leverages MMScan's captions to train grounding and 3D-LLM models, leading to a 7.17% increase in Average Precision (AP) and state-of-the-art performance on existing visual grounding and question-answering benchmarks. This approach enables enhanced instruction following performance in real-world scenarios . The study emphasizes the importance of scaling up 3D-text data pairs to advance multi-modal 3D learning. Initiatives like 3D-VisTA and EmbodiedScan aim to generate scene descriptions, collect more scenes, and annotate additional objects to scale up annotations to millions. However, the paper notes that previous works lack explicit hierarchical information in 3D scenes, such as different granularities of grounding entities, and highlights the need for human involvement in annotation scaling to serve as effective benchmarks for 3D-LLMs . The paper introduces innovative characteristics and advantages compared to previous methods in the field of multi-modal 3D scene understanding and language-grounded annotations . One key characteristic is the establishment of two multi-modal 3D perception benchmarks: visual grounding and question-answering, which incorporate single target and inter-target relationships with 5 sub-classes for comprehensive model evaluation . These benchmarks provide a significant advantage by offering a large number of samples, 1.28M and 1.76M on each benchmark, respectively, to assess model capabilities from various aspects .

In terms of advantages over previous methods, the paper addresses the limitations of existing benchmarks by highlighting the lower performance of visual grounding models and unsatisfactory results of current 3D-LLMs on their benchmarks . By leveraging meta-annotations and data-driven instruction tuning, the paper achieves a significant improvement in accuracy, up to 25.6%, on the question-answering benchmark . This improvement demonstrates the effectiveness of the proposed approach in enhancing model performance and instruction following in real-world scenarios .

Furthermore, the paper leverages MMScan's captions to train grounding and 3D-LLM models, resulting in a 7.17% increase in Average Precision (AP) and state-of-the-art performance on existing visual grounding and question-answering benchmarks . This approach not only enhances model performance but also enables better instruction following in practical applications . The integration of meta-annotations into scene-level captions provides a valuable resource for training 3D grounding and large language models, offering a comprehensive and effective approach to multi-modal 3D learning .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of multi-modal 3D scene datasets with hierarchical grounded language annotations. Noteworthy researchers in this field include the authors of the paper "MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations" . The key to the solution mentioned in the paper involves generating grounded scene-level captions from meta-annotations and integrating them to train 3D-LLMs efficiently to understand 3D scenes with hierarchical grounding capability .


How were the experiments in the paper designed?

The experiments in the paper were designed by establishing two multi-modal 3D perception benchmarks: visual grounding and question-answering. These benchmarks were created based on meta-annotations, generating samples for evaluation. The samples followed two streams: a single target and inter-target relationships, with 5 sub-classes for different aspects. This resulted in 1.28M and 1.76M samples on each benchmark, respectively, to assess the model's capabilities comprehensively .

The experiments evaluated representative baselines on the benchmarks and highlighted emerging challenges. The performance of visual grounding models was observed to be lower compared to existing benchmarks, indicating the complexity of understanding prompts entwined with spatial and attribute comprehension. Suggestions were made to enhance semantic understanding by including the image modality and improving candidate object selection. For the question-answering benchmark, current 3D-LLMs showed unsatisfactory results, with a significant 25.6% accuracy improvement achieved through data for instruction tuning .

Furthermore, the paper leveraged MMScan's captions to train grounding and 3D-LLM models, leading to a 7.17% AP increase and state-of-the-art performance on visual grounding and question-answering benchmarks. This approach notably enhanced instruction following performance in diverse scenarios .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is MMScan, a Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations . The code for the dataset is open source and can be accessed on GitHub at the following link: https://github.com/open-compass/opencompass .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper establishes two multi-modal 3D perception benchmarks focusing on visual grounding and question-answering, generating a large number of samples for evaluation . The benchmarks include single target and inter-target relationships, covering different aspects to assess the model's capabilities comprehensively .

The evaluation of representative baselines on these benchmarks revealed that the performance of visual grounding models was notably lower compared to existing benchmarks, highlighting the challenges in understanding complex prompts that involve spatial and attribute comprehension . The results also indicated the potential for enhancing semantic understanding by incorporating the image modality and improving candidate object selection .

Furthermore, the question-answering benchmark showed unsatisfactory results with current 3D-LLMs but demonstrated a significant improvement of up to 25.6% accuracy through data for instruction tuning . Leveraging the captions from MMScan for training grounding and 3D-LLM models led to a substantial increase in performance, achieving state-of-the-art results in visual grounding and question-answering benchmarks, ultimately enhancing instruction following capabilities .


What are the contributions of this paper?

The contributions of the paper include the establishment of two multi-modal 3D perception benchmarks: visual grounding and question-answering. These benchmarks are generated based on meta-annotations, incorporating single target and inter-target relationships with 5 sub-classes to evaluate the model's capabilities comprehensively . The paper provides valuable resources for training 3D grounding and large language models by seamlessly integrating retained correspondence information of meta-annotations into scene-level captions . Additionally, the paper evaluates representative baselines on the benchmarks, highlighting challenges in understanding complex prompts that require spatial and attribute comprehension, and suggests directions for improvement such as enhancing semantic understanding through the image modality and refining candidate object selection . Furthermore, the paper reports significant improvements in the performance of visual grounding and question-answering models by leveraging MMScan's captions for training, resulting in state-of-the-art performance and better instruction following capabilities in diverse scenarios .


What work can be continued in depth?

To further advance the field of multi-modal 3D scene understanding, one area that can be explored in depth is the development of datasets with explicit hierarchical information in 3D scenes, encompassing different levels of grounding entities . This would involve creating datasets that go beyond object annotations and incorporate detailed hierarchical structures within scenes, enabling more comprehensive and nuanced understanding of spatial relationships and attributes within the environment. By introducing hierarchical information, researchers can enhance the complexity and richness of the data, leading to more robust and sophisticated models for 3D scene understanding and language grounding .

Tables
7
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