Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning (2410.00255v2)

Abstract: Recent advancements in 3D LLMs (3DLLMs) have highlighted their potential in building general-purpose agents in the 3D real world, yet challenges remain due to the lack of high-quality robust instruction-following data, leading to limited discriminative power and generalization of 3DLLMs. In this paper, we introduce Robin3D, a powerful 3DLLM trained on large-scale instruction-following data generated by our novel data engine, Robust Instruction Generation (RIG) engine. RIG generates two key instruction data: 1) the Adversarial Instruction-following data, which features mixed negative and positive samples to enhance the model's discriminative understanding. 2) the Diverse Instruction-following data, which contains various instruction styles to enhance model's generalization. As a result, we construct 1 million instruction-following data, consisting of 344K Adversarial samples, 508K Diverse samples, and 165K benchmark training set samples. To better handle these complex instructions, Robin3D first incorporates Relation-Augmented Projector to enhance spatial understanding, and then strengthens the object referring and grounding ability through ID-Feature Bonding. Robin3D consistently outperforms previous methods across five widely-used 3D multimodal learning benchmarks, without the need for task-specific fine-tuning. Notably, we achieve a 7.8\% improvement in the grounding task (Multi3DRefer) and a 6.9\% improvement in the captioning task (Scan2Cap).

• The paper introduces a robust instruction data engine (RIG) that generates adversarial and diverse samples to enhance model discrimination and generalization.
• The model enhancements, including RAP and IFB, improve spatial reasoning and object grounding in complex 3D tasks.
• Performance gains on multiple benchmarks demonstrate Robin3D’s potential for advancing embodied AI without task-specific fine-tuning.

An Overview of Robin3D: Enhancing 3D LLMs Through Robust Instruction Tuning

The paper presents Robin3D, a 3D LLM (3DLLM) designed to address the current challenges in developing general-purpose AI agents for the 3D real world. Despite advancements in the field, the discriminative power and generalization ability of 3DLLMs have been hindered by a lack of high-quality instruction-following data. Robin3D tackles this by employing a novel data engine, the Robust Instruction Generation (RIG) engine, which provides a robust dataset composed of adversarial and diverse instruction-following data.

Key Contributions

1. Instruction Data Generation:
   • Adversarial Instruction Data: The RIG engine generates data that includes both positive and negative samples, enhancing the model's discriminative understanding. This data is designed to decouple the memorized positive pairs and test the model’s ability to differentiate between positive and adversarial scenarios.
   • Diverse Instruction Data: This dataset features a variety of instruction styles, enhancing the model's generalization capabilities. It leverages ChatGPT to diversify linguistic expressions, thereby improving the model's ability to understand a wide array of instructions.
2. Model Enhancements:
   • Relation-Augmented Projector (RAP): This component is introduced to enrich spatial relationship understanding, integrating object-centric features with scene-level context and positional information.
   • ID-Feature Bonding (IFB): Enhances the connection between object identifiers and their features, vital for accurate object referring and grounding in complex instructional tasks.

Performance and Implications

Robin3D showcases significant improvements over previous methods on five 3D multimodal learning benchmarks, without the need for task-specific fine-tuning. The paper highlights notable performance gains, such as a 7.8% improvement on the Multi3DRefer grounding task and a 6.9% improvement on the Scan2Cap captioning task. These results underscore the effectiveness of the robust training datasets and model enhancements.

The implications of this work are substantial both theoretically and practically. Theoretically, Robin3D pushes the boundary of Spatial Intelligence by leveraging robust training data and model innovations that allow for more effective interaction with 3D environments. Practically, these advancements pave the way for the development of AI agents capable of performing complex tasks in real-world 3D settings without extensive task-specific customization.

Future Directions

Looking forward, the methodologies introduced in Robin3D could impact future research in AI by inspiring further exploration into generating robust and diverse datasets for training models. Additionally, the integration of adversarial data and enhanced feature bonding may be extended to other domains beyond 3D environments, promoting advancements in general AI capabilities.

This work sets a foundation for future developments in Embodied AI and robotic agents, where understanding and reasoning in 3D spaces are paramount. As AI continues to progress, the principles outlined in Robin3D could serve as a critical component in developing more versatile and robust AI systems capable of understanding and interacting with the world in three dimensions.