arXiv:2607.04079v1 Announce Type: cross Abstract: Recent Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on 2D question answering tasks. However, extending these models to the 3D question answering remains challenging, as they typically require multiple views of the scene, which incurs substantial computational cost at inference. To mitigate this issue, existing solutions rely on strategic frame selection or token-merging algorithms that require preprocessing in advance all frames of the scene, i.e., an offline fashion. In contrast, we propose the first onlin
The proliferation of Multi-modal Large Language Models highlights the need for more efficient processing of 3D data, pushing researchers to address existing computational bottlenecks.
This development improves the efficiency of 3D question answering, a critical step for integrating LLMs into complex physical environments and robotic applications.
The ability to process 3D scenes 'online' rather than 'offline' significantly reduces latency and computational overhead for 3D MLLMs, making real-time applications more feasible.
- · AI hardware manufacturers
- · Robotics companies
- · Developers of 3D AI applications
- · Companies reliant on inefficient 3D data pipelines
- · Cloud providers charging per compute hour for inefficient tasks
More efficient 3D model inference for MLLMs reduces operational costs and expands application areas.
Improved real-time 3D understanding accelerates the development and deployment of autonomous systems in complex environments.
The integration of efficient 3D perception into AI agents could lead to more sophisticated and context-aware AI assistants capable of interacting with the physical world.
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Read at arXiv cs.AI