AREA3D：融合前馈三维感知与视觉语言引导的主动重建智能体
AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance
把前馈三维模型（如 VGGT 类）与不确定性建模解耦，用视觉语言模型提供语义先验来指导下一最佳视角选择，可直接迁移到主动式三维扫描、机器人探索和稀疏视角重建流程中。
原文依据 decouples view uncertainty modeling from feed-forward reconstruction, enabling precise uncertainty estimation without online optimization
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论主动三维重建使智能体能够自主选择视角，从而高效地构建准确且完整的场景几何，而不是被动地从预先采集的图像中重建场景。现有的主动重建方法往往依赖几何启发式规则，可能产生冗余观测却无法提升重建质量。为解决这一问题，我们提出 AREA3D，一种利用前馈三维模型与视觉语言引导的主动重建智能体。该框架将视角不确定性建模与前馈重建解耦，无需在线优化即可实现精确的不确定性估计。此外，集成的视觉语言模型提供高层语义引导，使探索能够超越纯几何线索。在场景级与物体级基准（Replica 与 OmniObject3D）上的大量实验表明，AREA3D 取得了最优的重建精度，尤其在稀疏视角下表现突出。
英文原文摘要 1013 字符
Active 3D reconstruction enables an agent to autonomously select viewpoints to build accurate and complete scene geometry efficiently, rather than passively reconstructing scenes from pre-collected images. Existing active reconstruction methods often rely on geometric heuristics, which may result in redundant observations without improving reconstruction quality. To address this, we propose AREA3D, an active reconstruction agent for 3D reconstruction by leveraging feed-forward 3D models and vision-language guidance. The framework decouples view uncertainty modeling from feed-forward reconstruction, enabling precise uncertainty estimation without online optimization. Moreover, the integrated Vision-Language Model provides high-level semantic guidance that guides exploration beyond purely geometric cues. Extensive experiments on both scene-level and object-level benchmarks (Replica and OmniObject3D) demonstrate that AREA3D achieves state-of-the-art reconstruction accuracy, especially in sparse views.