非自体参照感知器：通过帧实例化解耦非自体参照推理与自体参照视觉先验
Allocentric Perceiver: Disentangling Allocentric Reasoning from Egocentric Visual Priors via Frame Instantiation
AI总结：
非自体参照感知器通过帧实例化解耦非自体参照推理与自体参照视觉先验，提升空间推理任务性能。
摘要：
随着视觉-语言导航/行动等空间任务需求的增加，视觉-语言模型（VLMs）中的非自体参照感知能力受到越来越多的关注。然而，VLMs在需要显式视角转换的非自体参照空间查询上仍然脆弱，其中答案依赖于在目标参照系中进行推理，而不是观察到的摄像头视角。因此，我们引入了非自体参照感知器，这是一种无需训练的策略，它利用现成的几何专家从一个或多个图像中恢复度量3D状态，然后实例化一个与指令语义意图对齐的非自体参照参考系。通过确定性地将重建的几何转换到目标参照系，并提示骨干VLM使用结构化的、基于几何的表示，非自体参照感知器将心理旋转从隐式推理转移到显式计算。我们在多个骨干家族上评估了非自体参照感知器，在空间推理基准上观察到一致且显著的提升（约10%）在非自体参照任务上，同时保持强大的自体参照性能，并超越了空间感知微调模型和最先进的开源和专有模型。
英文摘要：
With the rising need for spatially grounded tasks such as Vision-Language Navigation/Action, allocentric perception capabilities in Vision-Language Models (VLMs) are receiving growing focus. However, VLMs remain brittle on allocentric spatial queries that require explicit perspective shifts, where the answer depends on reasoning in a target-centric frame rather than the observed camera view. Thus, we introduce Allocentric Perceiver, a training-free strategy that recovers metric 3D states from one or more images with off-the-shelf geometric experts, and then instantiates a query-conditioned allocentric reference frame aligned with the instruction's semantic intent. By deterministically transforming reconstructed geometry into the target frame and prompting the backbone VLM with structured, geometry-grounded representations, Allocentric Perceriver offloads mental rotation from implicit reasoning to explicit computation. We evaluate Allocentric Perciver across multiple backbone families on spatial reasoning benchmarks, observing consistent and substantial gains ($\sim$10%) on allocentric tasks while maintaining strong egocentric performance, and surpassing both spatial-perception-finetuned models and state-of-the-art open-source and proprietary models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
