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arXiv · 2610.06611

Lens3D: Target-Conditioned Visual Foveation for Fine-Grained 3D Understanding

Junming Huang·Zini Chen·Shuaiying Hou·Chi Wang·Qiang Dai·Weiwei Xu
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Abstract

Existing 3D large language models often overlook fine-grained attributes and less visually salient objects and parts, even when relevant evidence is present in scene videos. We introduce Lens3D to improve fine-grained object understanding through external visual assistance and knowledge transfer. Its LensUnd pipeline adopts 3D localization to select informative, complementary views for an external 2D vision-language model, supporting fine-grained object captioning, small-object grounding, and fine-grained object question answering. LensDistill transfers the resulting fine-grained knowledge to 3D LLMs through detailed caption supervision, enabling captioning from native inputs without external VLM calls. We also construct LensBench, a held-out evaluation set of 2,068 objects with three silver-standard reference descriptions per object. Experiments with Video-3D LLM and 3DRS demonstrate that LensDistill substantially improves fine-grained object captioning while preserving existing grounding and scene-level QA performance. These results establish the feasibility of transferring externally acquired fine-grained knowledge into native 3D LLMs.

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Junming Huang, Zini Chen, Shuaiying Hou, Chi Wang, Qiang Dai, Weiwei Xu. 2026-10-05. Lens3D: Target-Conditioned Visual Foveation for Fine-Grained 3D Understanding. https://arxiv.org/abs/2610.06611

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