- The paper proposes the 3DRS framework to enhance MLLMs by leveraging pretrained 3D foundation models for improved scene comprehension.
- It employs voxel-based feature similarity across multiple views to demonstrate that stronger 3D awareness leads to better downstream task performance.
- The study’s results show notable performance gains in scene understanding, offering promising applications in robotics, AR, and spatial reasoning.
Enhancing 3D Scene Understanding with MLLM and 3D Foundation Models
The paper "MLLMs Need 3D-Aware Representation Supervision for Scene Understanding" focuses on advancing the capabilities of multimodal LLMs (MLLMs) in three-dimensional (3D) scene understanding, which remains limited due to the lack of explicit 3D data during the pretraining process. Recent efforts have concentrated on leveraging 3D reasoning by building upon MLLMs’ robust 2D pretraining foundations. This paper aims to address the existing gap by introducing a novel framework called 3D Representation Supervision (3DRS) that utilizes supervision from pretrained 3D foundation models, enhancing MLLMs’ ability to learn and understand 3D scenes.
The authors begin by exploring three critical questions surrounding the 3D-awareness of MLLMs: how to evaluate their proficiency in 3D representation learning, the relationship between 3D learning quality and downstream task performance, and effective methods for augmenting 3D representation learning within MLLM frameworks. Through extensive evaluations involving multi-view correspondences, the researchers establish a positive correlation between 3D-aware representation quality and the performance on diverse tasks, emphasizing the importance of incorporating 3D feature learning into MLLMs.
For assessing 3D feature proficiency, the authors employ a voxel-based feature similarity approach across multiple views, testing representative MLLMs including LLaVA-Next-Video, LLaVA-One-Vision, and Qwen2-VL on datasets such as ScanRefer and ScanQA. Their findings indicate that higher correspondence scores, reflecting stronger 3D awareness, correlate with superior downstream performance.
To enhance 3D representation capabilities of MLLMs, the authors introduce the 3DRS framework, which aligns MLLMs’ visual outputs with the features extracted from 3D foundation models such as VGGT and FLARE. This is achieved by computing visual feature correspondences across different views, supervised by the rich 3D priors embedded in these foundation models. The 3DRS framework delivers significant performance improvements across various benchmarks, validating its effectiveness in strengthening MLLMs’ 3D awareness without incurring additional computational overhead.
The practical and theoretical implications of this research are substantial. Enhancing 3D understanding can notably benefit applications involving robotic navigation, augmented reality, and spatial reasoning. Moreover, this paper broadens the potential for more integrated multimodal AI systems that effectively combine visual and spatial information. Future research can further explore integrating 3D-aware learning into the pretraining stages of MLLMs, potentially leading to more robust models capable of deeper and more generalized 3D comprehension.
In summary, the paper lays a compelling foundation for improving scene understanding in MLLMs through 3D-aware representation learning facilitated by 3D foundation models. This approach aligns with broader efforts to refine AI models’ capabilities across different modalities and enhance their applicability in complex real-world tasks.