M3: 3D-Spatial MultiModal Memory (2503.16413v1)

Abstract: We present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception. By integrating 3D Gaussian Splatting techniques with foundation models, M3 builds a multimodal memory capable of rendering feature representations across granularities, encompassing a wide range of knowledge. In our exploration, we identify two key challenges in previous works on feature splatting: (1) computational constraints in storing high-dimensional features for each Gaussian primitive, and (2) misalignment or information loss between distilled features and foundation model features. To address these challenges, we propose M3 with key components of principal scene components and Gaussian memory attention, enabling efficient training and inference. To validate M3, we conduct comprehensive quantitative evaluations of feature similarity and downstream tasks, as well as qualitative visualizations to highlight the pixel trace of Gaussian memory attention. Our approach encompasses a diverse range of foundation models, including vision-LLMs (VLMs), perception models, and large multimodal and LLMs (LMMs/LLMs). Furthermore, to demonstrate real-world applicability, we deploy M3's feature field in indoor scenes on a quadruped robot. Notably, we claim that M3 is the first work to address the core compression challenges in 3D feature distillation.

Summary of "M3: 3D-Spatial Multimodal Memory"

The paper presents the architectural and methodological framework for a novel approach named 3D Spatial Multimodal Memory (M3). Addressing the computational and alignment challenges associated with previous methods in multimodal memory systems, M3 integrates 3D Gaussian Splatting with foundation models to store and render high-dimensional features in static scene videos efficiently. This synthesis enables the retention of semantic understanding in 3D scenes, a limitation observed in existing models such as NeRF and 3DGS.

Key Contributions and Methodology

The paper delineates two primary deficiencies in prior works: the computational constraints limiting high-dimensional feature storage and the resultant misalignment or information loss during feature translation. To overcome these challenges, M3 employs the following strategic innovations:

1. 3D Gaussian Splatting with Principle Scene Components (PSC) and Queries: The introduction of PSC mitigates redundancy in video data by compressing extracted features into a memory bank, which represents each scene's essential information. Simultaneously, principle queries optimize the rendering of these features into the 3D scene structure. This structure allows for efficient training and inference across various multimodal tasks.
2. Gaussian Memory Attention: M3 utilizes a novel attention mechanism to leverage these principal scene components and queries, aligning them with the high-dimensional embeddings of foundation models. This mechanism preserves the expressiveness of the foundation models while maintaining a low-dimensional representation aligned with the Gaussian structure.
3. Integration Across Foundation Models: By encompassing a wide range of foundation models, M3 extends beyond the limitations of single-foundation configurations. This diversity enriches the memory's ability to handle multiple visual and language modalities, covering a spectrum from vision-LLMs to large multimodal LLMs.

Evaluations and Results

Evaluating M3 across various datasets and metrics, the authors highlight a consistent outperforming of traditional feature distillation techniques. The experimental results, characterized by improved feature similarity metrics and downstream task performance, support M3's efficacy. Notably, M3's architecture reduces the computational overhead typical in feature distillation methods, achieving superior performance with fewer parameters.

The paper also demonstrates M3's applicability in real-world implementations via deployment on a quadruped robot. This deployment showcases M3's potential for practical applications, such as robotic processing in indoor scenes, further confirming the model's adaptability and efficiency.

Implications for Future Research

M3 provides a significant contribution to the intersection of computer vision and multimodal AI systems by addressing critical geospatial memory challenges in video processing. It shifts the paradigm towards real-time compatibility and seamless semantic integration, setting a new precedent for future research in AI memory systems.

Potential future developments might explore integrating dynamic scene understanding, leveraging M3's framework for scenarios involving temporal variations. Additionally, refining the Gaussian memory attention mechanism could provide further performance enhancements by improving the alignment with the intrinsic multi-faceted nature of AI foundation models.

In conclusion, M3 stands as a robust and flexible system, marking a substantial advancement in the field of multimodal memory systems. It provides a scalable and computationally efficient framework that can adapt to various AI tasks, suggesting a promising direction for research and application within this domain.