Task-oriented Sequential Grounding in 3D Scenes
Overview
Grounding natural language in physical 3D environments is critical for advancing embodied AI. Traditional datasets and models for 3D visual grounding have primarily focused on static, object-centric descriptions. However, these approaches fall short in dynamic, task-oriented scenarios that are crucial for practical applications. To bridge this gap, the paper introduces a new task named Task-oriented Sequential Grounding in 3D scenes, alongside a corresponding dataset called SG3D. This task requires an agent to follow detailed, step-by-step instructions to locate a sequence of target objects in indoor scenes, thereby facilitating complex daily activities.
Key Contributions
- Introduction of a Novel Task and Dataset:
- The authors propose Task-oriented Sequential Grounding in 3D scenes, which extends beyond static object identification to encompass dynamic and sequential task execution.
- The SG3D dataset is introduced, containing 22,346 tasks with 112,236 steps across 4,895 real-world 3D scenes. This dataset is constructed using RGB-D scans from various 3D scene datasets and an automated task generation pipeline, followed by human verification for quality assurance.
- Adaptation and Evaluation of State-of-the-Art Models:
- Three state-of-the-art 3D visual grounding models—3D-VisTA, PQ3D, and LEO—are adapted for the sequential grounding task.
- The models are evaluated on the SG3D dataset, revealing significant challenges in task-oriented sequential grounding, even for state-of-the-art models.
Dataset Construction
The SG3D dataset is constructed through a combination of various 3D scene datasets, including ScanNet, ARKitScenes, 3RScan, and more, which encompass a diverse range of indoor environments. The tasks in SG3D are generated using a combination of scene graphs and GPT-4 to ensure diversity and quality. Human verification ensures that the generated tasks are appropriate and accurately represent target objects in the scene.
Experimental Results and Analysis
The experimental results underscore the challenges faced by current models in adapting to sequential grounding tasks:
- Zero-shot Performance:
- Models like 3D-VisTA and PQ3D, when evaluated in a zero-shot setting, show relatively low step accuracy (s-acc) and task accuracy (t-acc), indicating the insufficiency of pre-training on non-sequential tasks.
- Fine-tuning:
- Fine-tuning yields significant performance improvements for all models. For instance, 3D-VisTA’s task accuracy improves from 8.3% to 30.6%, while PQ3D shows a similar trend. LEO, the 3D LLM, achieves the highest performance post fine-tuning, with a step accuracy of 62.8% and a task accuracy of 34.1%.
- Despite these improvements, task accuracies remain below 40%, highlighting the persistent challenges in achieving consistent sequential grounding.
- Model Comparison:
- The 3D LLM model, LEO, consistently outperforms dual-stream and query-based models across all datasets, particularly in task accuracy, underscoring its superior ability to handle sequential dependencies.
- Qualitative Insights:
- The ability of LEO to perform sequential grounding is illustrated through qualitative examples. However, challenges remain in maintaining sequential consistency and understanding complex object relationships.
Implications and Future Work
The introduction of the SG3D dataset and the task of Task-oriented Sequential Grounding in 3D scenes has significant implications for the development of more capable and context-aware embodied AI systems. The findings from this paper indicate several avenues for future research:
- Enhancing Sequential Reasoning:
- Future models must focus on improving sequential reasoning capabilities to handle complex task-oriented instructions effectively.
- Incorporating Common Sense Knowledge:
- Integrating common sense reasoning into models can help overcome challenges in understanding complex object relations and task contexts.
- Improved Model Architectures:
- Adopting more advanced architectures, such as chain-of-thought reasoning and reflection mechanisms, could further enhance model performance.
- Practical Deployments:
- Adapting sequential grounding models for real-world applications, such as robotics and assistive technologies, remains a crucial goal, requiring further development of reliable, context-aware AI systems.
Conclusion
This paper presents a significant step towards more dynamic and practical applications of 3D visual grounding through the introduction of Task-oriented Sequential Grounding and the SG3D dataset. While current models face substantial challenges in this domain, the findings offer a clear direction for future research to develop more robust and capable embodied AI systems.