Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation
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 collection at both the view and token levels. Specifically, we combine pixel features with camera parameters and assess view importance based on both s...
| Authors | Dongsheng Wang, Dawei Su, Hui Huang |
|---|---|
| Primary category | cs.CV |
| Published | 2 June 2026 |
| Last updated at source | 4 June 2026 |
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