Localizing objects and parts from natural language in 3D space is essential for robotics, AR, and embodied AI, yet existing methods face a trade-off between the accuracy and geometric consistency of per-scene optimization and the efficiency of feed-forward inference. We present TrianguLang, a feed-forward framework for 3D localization that requires no camera calibration at inference. Unlike prior methods that treat views independently, we introduce Geometry-Aware Semantic Attention (GASA), which utilizes predicted geometry to gate cross-view feature correspondence, suppressing semantically-plausible but geometrically-inconsistent matches without requiring ground-truth poses. Validated on five benchmarks including ScanNet++ and uCO3D, TrianguLang achieves state-of-the-art feed-forward text-guided segmentation and localization, reducing user effort from $O(N)$ clicks to a single text query. The model processes each frame at 1008x1008 resolution in $\sim$57ms ($\sim$18 FPS) without optimization, enabling practical deployment for interactive robotics and AR applications. Code and checkpoints are available at https://cwru-aism.github.io/triangulang/.

翻译：从自然语言在三维空间中定位物体及其部件是机器人技术、增强现实和具身人工智能的关键能力，然而现有方法在场景级优化的精度与几何一致性及前馈推理的效率之间存在权衡。本文提出TrianguLang——一种无需推理时相机标定的前馈式三维定位框架。不同于以往独立处理视图的方法，我们引入几何感知语义注意力机制（GASA），该机制利用预测的几何信息门控跨视图特征对应关系，在无需真实位姿的情况下抑制语义合理但几何不一致的匹配。在包括ScanNet++和uCO3D在内的五个基准数据集上的验证结果表明，TrianguLang实现了面向前馈文本引导的语义分割与定位的最优性能，将用户操作从$O(N)$次点击简化为单次文本查询。该模型在1008×1008分辨率下以$\sim$57ms（$\sim$18 FPS）处理每帧图像且无需额外优化，从而支持交互式机器人和增强现实应用的实际部署。代码与模型权重已开源至https://cwru-aism.github.io/triangulang/。