3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination

Overview of "3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination"

The paper "3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination" addresses the significant challenge of integrating LLMs with 3D perception systems. This integration is crucial for the advancement of embodied artificial intelligence (EAI) and robotics, aiming to create systems that can navigate and interact with the physical world effectively. The paper presents two major contributions: the introduction of the 3D-GRAND dataset and the 3D-POPE benchmark.

3D-GRAND Dataset

3D-GRAND is a pioneering, large-scale dataset designed to enhance the capabilities of 3D language models (3D-LLMs). This dataset comprises 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. These scenes are sourced from synthetic datasets such as 3D-FRONT and Structured3D, which offer high-quality, diverse indoor environments.

Key Features of 3D-GRAND

1. Scale and Density: With over 40,000 scenes and 6.2 million annotations, 3D-GRAND is the largest 3D-text dataset to date. The dense grounding aspect ensures that each annotation is meticulously tied to specific objects or regions within the 3D scenes, providing fine-grained contextual understanding.
2. Diverse Language Tasks: The dataset supports a range of tasks, including 3D-grounded object reference, scene descriptions, and QA, enabling comprehensive evaluation of 3D-LLMs across different scenarios.
3. Quality Assurance: Extensive human evaluations and a sophisticated annotation process involving GPT-4 ensure the dataset's annotations are of high quality, reducing potential issues such as hallucination and incorrect grounding.

3D-POPE Benchmark

3D-POPE (3D Polling-based Object Probing Evaluation) is introduced to systematically evaluate and quantify hallucination in 3D-LLMs. Hallucination in this context refers to the models' tendency to generate descriptions of objects that do not exist in the given scenes.

Benchmark Characteristics

1. Evaluation Protocol: 3D-POPE poses existence questions to 3D-LLMs and evaluates their responses (Yes/No) for accuracy, precision, recall, F1 score, and hallucination metrics. This systematic approach allows for fair and comprehensive comparisons across different models.
2. Robust Sampling Strategies: The benchmark includes three sampling strategies for selecting non-existent objects—Random, Popular, and Adversarial—each designed to challenge the models' robustness and susceptibility to hallucinations.

Experimental Results

The paper demonstrates the efficacy of 3D-GRAND and 3D-POPE through extensive experiments:

1. Grounding Accuracy: Models trained on 3D-GRAND show significant improvements in grounding accuracy, particularly in complex scenarios with multiple distractors. On the ScanRefer benchmark, the 3D-GRAND model achieved state-of-the-art zero-shot performance, with notable improvements in [email protected] and [email protected] metrics over existing models.
2. Reduction in Hallucination: Training with 3D-GRAND substantially reduces hallucinations in 3D-LLMs. In the 3D-POPE evaluation, the model trained on 3D-GRAND exhibited high precision and accuracy, especially in the Random sampling scenario, showcasing its robustness against generating non-existent object references.
3. Data Scaling and Sim-to-Real Transfer: The study reveals a clear scaling effect, where increasing the volume of densely-grounded data leads to better performance in grounding tasks and reduced hallucination rates. The model also shows promising sim-to-real transfer capabilities, indicating that training on synthetic data can be effectively transferred to real-world scenarios.

Implications and Future Directions

The introduction of 3D-GRAND and 3D-POPE paves the way for significant advancements in embodied AI. The dataset offers ample resources for training models that are better grounded in 3D space, enhancing their ability to interact with and comprehend the physical world. This has profound implications for the development of robots and intelligent agents capable of performing complex tasks in real environments.

Future research could explore further scaling of synthetic data generation, improving sim-to-real transfer methods, and investigating new architectural advancements in 3D-LLMs to leverage the dense grounding provided by datasets like 3D-GRAND. Additionally, enhancing the robustness of benchmarks such as 3D-POPE to cover more diverse and challenging scenarios will be crucial for comprehensively evaluating 3D-LLMs.

In summary, this paper makes substantial contributions to the field of EAI and offers valuable resources and insights that set a solid foundation for future research and development in 3D language models.