OSMa-Bench++：面向操作任务的语义映射开放基准测试，使用提示生成的合成场景
OSMa-Bench++: Toward Open-Ended Benchmarking of Semantic Mapping for Manipulation with Prompt-Generated Synthetic Scenes
AI总结：
本文扩展OSMa-Bench，通过提示生成合成室内场景实现可控基准测试，并提出一种基于提示的VQA类别，用于语义映射方法在杂乱、小物体、部分遮挡和光照变化等条件下的压力测试。
摘要：
语义映射方法越来越多地被用作下游机器人推理和操作的中间场景表示，但它们的评估仍然很大程度上依赖于固定的基准数据集，这些数据集对操作相关边缘情况的覆盖有限。在这项工作中，我们将OSMa-Bench扩展到使用提示生成的合成室内场景进行可控基准测试。我们的流程自动生成场景描述，使用SceneSmith合成相应环境，并将生成的资产适配为OSMa-Bench兼容的仿真格式。这种适配需要一个非平凡的中层，包括语义归一化、材质和纹理修复、着色器回退策略、地面处理、导航设置和受控光照配置。所提出设置的一个关键优势是原始场景生成提示是预先已知的，因此可以作为预期场景的辅助语义规范。我们利用这一特性，将OSMa-Bench的VQA组件扩展了一个基于提示的问题类别。由此产生的框架支持在杂乱、小物体、部分遮挡和光照变化等条件下对语义场景表示进行有针对性的压力测试，并使基准测试更具可扩展性，更好地与下游操作需求对齐。我们的代码可在https://github.com/be2rlab/OSMa-Bench-v2获取。
英文摘要：
Semantic mapping methods are increasingly used as intermediate scene representations for downstream robotic reasoning and manipulation, yet their evaluation is still largely tied to fixed benchmark datasets with limited coverage of manipulation-relevant corner cases. In this work, we extend OSMa-Bench toward controllable benchmarking with prompt-generated synthetic indoor scenes. Our pipeline automatically generates scene descriptions, synthesizes corresponding environments with SceneSmith, and adapts the resulting assets into an OSMa-Bench-compatible simulation format. This adaptation requires a nontrivial intermediate layer, including semantic normalization, material and texture repair, shader fallback policies, floor handling, navigation setup, and controlled lighting configuration. A key advantage of the proposed setup is that the original scene-generation prompt is known in advance and can therefore serve as an auxiliary semantic specification of the intended scene. We use this property to extend the VQA component of OSMa-Bench with a prompt-grounded question category. The resulting framework supports targeted stress-testing of semantic scene representations under conditions such as clutter, small objects, partial occlusions, and lighting variation, and makes benchmarking more extensible and better aligned with downstream manipulation requirements. Our code is available at https://github.com/be2rlab/OSMa-Bench-v2.
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。
