Context-Nav：面向实例导航的上下文驱动探索与视角感知三维空间推理
Context-Nav: Context-Driven Exploration and Viewpoint-Aware 3D Spatial Reasoning for Instance Navigation
把长文本描述编码成稠密文本-图像对齐的价值图来给前沿点排序，作为探索先验；再用采样观察位姿、对齐局部坐标系做三维空间关系校验来判定候选，这套「价值图引导探索 + 视角感知关系验证」的零训练流程可直接迁移到具身导航与开放词表目标定位中。
原文依据 we compute dense text-image alignments for a value map that ranks frontiers
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论文本目标实例导航（TGIN）要求智能体将单条自由形式的描述解析为动作，从而在同类别干扰物中抵达正确的物体实例。本文提出 Context-Nav，将长篇幅的上下文描述从局部匹配线索提升为全局探索先验，并通过三维空间推理对候选进行验证。首先，我们计算稠密的文本-图像对齐以构建价值图，对前沿点进行排序——引导探索朝向与完整描述一致的区域，而非仅依赖早期检测结果。其次，在观测到候选后，我们执行视角感知的关系校验：智能体采样合理的观察者位姿，对齐局部坐标系，仅当空间关系能够从至少一个视角得到满足时才接受该目标。该流程无需任务特定的训练或微调；我们在 InstanceNav 与 CoIN-Bench 上取得了最优性能。消融实验表明：(i) 将完整描述编码进价值图可避免无效移动；(ii) 显式的视角感知三维验证可防止语义上合理但错误的停止。这表明，对于杂乱三维场景中的细粒度实例消歧，基于几何的空间推理是替代繁重策略训练或人在回路交互的一种可扩展方案。
英文原文摘要 1314 字符
Text-goal instance navigation (TGIN) asks an agent to resolve a single, free-form description into actions that reach the correct object instance among same-category distractors. We present Context-Nav, which elevates long, contextual captions from a local matching cue to a global exploration prior and verifies candidates through 3D spatial reasoning. First, we compute dense text-image alignments for a value map that ranks frontiers---guiding exploration toward regions consistent with the entire description rather than early detections. Second, upon observing a candidate, we perform a viewpoint-aware relation check: the agent samples plausible observer poses, aligns local frames, and accepts a target only if the spatial relations can be satisfied from at least one viewpoint. The pipeline requires no task-specific training or fine-tuning; we attain state-of-the-art performance on InstanceNav and CoIN-Bench. Ablations show that (i) encoding full captions into the value map avoids wasted motion and (ii) explicit, viewpoint-aware 3D verification prevents semantically plausible but incorrect stops. This suggests that geometry-grounded spatial reasoning is a scalable alternative to heavy policy training or human-in-the-loop interaction for fine-grained instance disambiguation in cluttered 3D scenes.