RG-SAN：用于端到端3D指代表达式分割的规则引导空间感知网络
RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation
AI总结：
提出规则引导空间感知网络RG-SAN，通过文本驱动定位模块和规则引导弱监督策略增强3D指代表达式分割的空间推理能力，在ScanRefer上mIoU提升5.1个百分点并显著增强鲁棒性。
摘要：
3D指代表达式分割（3D-RES）旨在通过将指代表达式与点云相关联来分割3D对象。然而，传统方法由于对实例的空间信息重视不足，经常遇到过度分割或错误分割的问题。在本文中，我们提出了一种规则引导空间感知网络（RG-SAN），仅利用目标实例的空间信息进行监督。该方法使网络能够准确描绘文本中描述的所有实体之间的空间关系，从而增强推理能力。RG-SAN由文本驱动定位模块（TLM）和规则引导弱监督（RWS）策略组成。TLM首先定位所有提及的实例，并迭代地细化其位置信息。RWS策略考虑到只有目标对象具有受监督的位置信息，采用依赖树规则来精确引导核心实例的定位。在ScanRefer基准上的广泛测试表明，RG-SAN不仅建立了新的性能基准，mIoU提高了5.1个百分点，而且在处理具有空间歧义的描述时表现出显著的鲁棒性提升。所有代码可在https://github.com/sosppxo/RG-SAN获取。
英文摘要：
3D Referring Expression Segmentation (3D-RES) aims to segment 3D objects by correlating referring expressions with point clouds. However, traditional approaches frequently encounter issues like over-segmentation or mis-segmentation, due to insufficient emphasis on spatial information of instances. In this paper, we introduce a Rule-Guided Spatial Awareness Network (RG-SAN) by utilizing solely the spatial information of the target instance for supervision. This approach enables the network to accurately depict the spatial relationships among all entities described in the text, thus enhancing the reasoning capabilities. The RG-SAN consists of the Text-driven Localization Module (TLM) and the Rule-guided Weak Supervision (RWS) strategy. The TLM initially locates all mentioned instances and iteratively refines their positional information. The RWS strategy, acknowledging that only target objects have supervised positional information, employs dependency tree rules to precisely guide the core instance's positioning. Extensive testing on the ScanRefer benchmark has shown that RG-SAN not only establishes new performance benchmarks, with an mIoU increase of 5.1 points, but also exhibits significant improvements in robustness when processing descriptions with spatial ambiguity. All codes are available at https://github.com/sosppxo/RG-SAN.
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。
