arXiv:2606.19733v1 Announce Type: cross Abstract: Efficiently retrieving specific 3D instances from large-scale scenes via natural language prompts remains a formidable challenge in multimedia analysis. Existing approaches predominantly follow a "scene-level embedding" paradigm, which requires distilling high-dimensional semantic features into every 3D primitive. This strategy suffers from a fundamental architectural bottleneck: memory and computational costs scale linearly with scene complexity, inevitably triggering out-of-memory (OOM) failures in city-scale environments. To address this bar
The paper addresses the scaling limitations of current 3D instance retrieval methods, a critical need as AI applications move towards larger, more complex real-world environments.
Efficient and scalable 3D instance retrieval is crucial for deploying AI in large-scale autonomous systems, robotics, and mixed reality, impacting data processing and operational capabilities.
This research introduces a training-free, scalable approach that could overcome memory and computational bottlenecks, enabling practical open-vocabulary 3D understanding in previously unmanageable scales.
- · AI agents
- · Robotics companies
- · Smart city developers
- · Gaming/VR industries
- · Traditional high-compute 3D processing pipelines
- · Companies reliant on bespoke 3D model training
Improved efficiency and accuracy in deploying 3D object recognition in expansive, real-world scenes.
Accelerated development of autonomous vehicles and robots with enhanced environmental understanding.
Emergence of new applications based on real-time, large-scale, open-vocabulary 3D interaction, potentially blurring physical and digital realities.
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Read at arXiv cs.AI