arXiv:2606.03100v1 Announce Type: cross Abstract: Recently, zero-shot 3D scene understanding via 2D Vision-Language Models (VLMs) has gained increasing research interest due to their promising spatial reasoning capabilities. Typically, multiple 2D views are sampled from a 3D point cloud and fed into pre-trained VLMs to answer a given question. This paradigm highlights the critical role of input context quality and raises the challenge of retaining as many task-relevant 3D details as possible under a limited input budget. We propose \texttt{KeyVT}, a hierarchical approach for input context coll
The proliferation of 3D data and the increasing capabilities of 2D Vision-Language Models are pushing the boundaries of zero-shot 3D scene understanding, creating a demand for more efficient data processing techniques.
Improved zero-shot 3D question answering enhances the autonomy of AI systems in complex physical environments, directly impacting applications from robotics to spatial computing with less need for pre-training.
The proposed 'KeyVT' method improves the efficiency and detail retention for 3D understanding, suggesting more robust and capable AI systems in environments where data budget or training data is limited.
- · AI developers
- · Robotics companies
- · Enhanced reality platforms
More accurate and versatile AI systems for interacting with 3D environments will emerge.
This improved understanding could accelerate the development and deployment of autonomous systems in diverse real-world applications.
Reduced need for extensive manual 3D data annotation and labeling for specific applications, lowering development costs and accelerating innovation cycles.
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Read at arXiv cs.LG