3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning (2411.17735v5)

Abstract: Constructing compact and informative 3D scene representations is essential for effective embodied exploration and reasoning, especially in complex environments over extended periods. Existing representations, such as object-centric 3D scene graphs, oversimplify spatial relationships by modeling scenes as isolated objects with restrictive textual relationships, making it difficult to address queries requiring nuanced spatial understanding. Moreover, these representations lack natural mechanisms for active exploration and memory management, hindering their application to lifelong autonomy. In this work, we propose 3D-Mem, a novel 3D scene memory framework for embodied agents. 3D-Mem employs informative multi-view images, termed Memory Snapshots, to represent the scene and capture rich visual information of explored regions. It further integrates frontier-based exploration by introducing Frontier Snapshots-glimpses of unexplored areas-enabling agents to make informed decisions by considering both known and potential new information. To support lifelong memory in active exploration settings, we present an incremental construction pipeline for 3D-Mem, as well as a memory retrieval technique for memory management. Experimental results on three benchmarks demonstrate that 3D-Mem significantly enhances agents' exploration and reasoning capabilities in 3D environments, highlighting its potential for advancing applications in embodied AI.

• The paper introduces SnapMem, a snapshot-based 3D scene memory using dual-level snapshots and co-visibility clustering for compact, rich scene representation beyond object-centric models.
• Experiments demonstrate SnapMem significantly improves embodied agents' performance in exploration and long-term reasoning tasks, outperforming existing methods in accuracy and path efficiency.
• SnapMem has practical implications for embodied AI applications like robot navigation and opens theoretical avenues for integration with advanced vision-language models.

Overview of SnapMem: Enhancing Embodied Exploration and Reasoning with 3D Scene Memory

The paper "SnapMem: Snapshot-based 3D Scene Memory for Embodied Exploration and Reasoning" introduces an innovative framework for constructing a 3D scene memory aimed at improving the exploration and reasoning capabilities of embodied agents. This framework addresses the limitations of existing scene representations by utilizing a snapshot-based approach for encoding rich visual information within compact memory structures, thus paving the way for extended exploration in complex environments.

SnapMem employs a dual-level representation system consisting of Memory Snapshots and Frontier Snapshots. Memory Snapshots encapsulate explored regions by capturing co-visible objects, their spatial arrangements, and contextual background details from single images. In contrast, Frontier Snapshots extend this representation to unexplored territories, facilitating active decision-making driven by potential discoveries.

Theoretical Implications and Methodology

One of the fundamental contributions of SnapMem is its sophisticated approach to scene representation that challenges conventional object-centric models, such as 3D scene graphs. These models have traditionally reduced scenes to discrete objects with predefined relational descriptions, often losing vital spatial configurations required for nuanced spatial reasoning. SnapMem circumvents these constraints by capturing holistic visual information directly from images, allowing for a more comprehensive understanding of spatial relations and context.

Furthermore, SnapMem introduces an incremental scene memory construction pipeline integrated with a frontier-based exploration strategy. By employing Co-Visibility Clustering, SnapMem organizes objects into clusters based on co-occurrence across multiple frames, selecting representative snapshots that encompass maximal contextual information. This method ensures minimal yet robust representation of scenes, addressing both the scalability and efficiency concerns associated with growing environments.

Experimental Results

Experiments conducted across various benchmarks demonstrate the efficacy of SnapMem. The novel representation system significantly enhances the performance of agents in tasks involving exploration and reasoning over long durations. Quantitatively, SnapMem outperforms existing approaches such as Explore-EQA and ConceptGraph in terms of both exploration accuracy and path efficiency, as evidenced by LLM-Match and SPL metrics improvements. These results affirm SnapMem's capability to perform efficient memory management and active exploration, vital for lifelong autonomy in embodied AI.

Practical Implications and Future Directions

Practically, SnapMem is poised to offer substantial improvements in real-world embodied AI applications, such as robot navigation and complex spatial reasoning tasks. Its ability to manage dynamic environments and adaptively explore new territories suggests potential for integration into domains requiring sustained environmental interaction.

From a theoretical perspective, SnapMem opens avenues for further research into snapshot-based reasoning models. Future work could explore the integration of SnapMem with advanced vision-LLMs, exploiting the rich visual features of snapshots for even deeper semantic and spatial reasoning. Additionally, enhancing the frontier-based exploration mechanisms to scale across multifloor environments can bolster its adaptability.

Overall, SnapMem represents a promising step towards a more nuanced and adaptable 3D scene memory system, addressing inherent limitations in current representations, and offering meaningful contributions to the field of embodied AI.