SeGPruner: Semantic-Geometric Visual Token Pruner for 3D Question Answering
Vision-language models (VLMs) have been widely adopted for 3D question answering (3D QA). In typical pipelines, visual tokens extracted from multiple viewpoints are concatenated with language tokens and jointly processed by a large language model (LLM) for inference. However, aggregating multi-view observations inevitably introduces severe token redundancy, leading to an overly large visual token set that significantly hinders inference efficiency under constrained token budgets. Visual token pruning has emerged as a prevalent strategy to address this issue. Nevertheless, most existing pruners are primarily tailored to 2D inputs or rely on indirect geometric cues, which limits their abili...
| Authors | Wenli Li, Kai Zhao, Haoran Jiang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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