SceneComplete：面向机器人操作的在杂乱真实世界环境中的开放世界三维场景补全
SceneComplete: Open-World 3D Scene Completion in Cluttered Real World Environments for Robot Manipulation
AI总结：
SceneComplete通过组合多种预训练感知模块，从单张RGB-D图像构建完整分割的三维场景模型，实现准确重建并支持稳健抓取提议生成。
摘要：
在日常杂乱环境中进行精细的机器人操作，需要准确理解三维场景，以便稳定可靠地抓取和放置物体，并避免与其他物体发生碰撞。通常，我们必须基于有限输入（如单张RGB-D图像）来构建复杂场景的三维解释。我们描述了SceneComplete，这是一个从单一视图构建完整、分割的三维场景模型的系统。SceneComplete是一种新颖的流水线，用于组合通用预训练感知模块（视觉-语言、分割、图像修复、图像到三维、视觉描述符和姿态估计），以获得高度准确的结果。我们在一个大型基准数据集上，相对于真值模型展示了其准确性和有效性，并表明其准确的整物体重建能够实现稳健的抓取提议生成，包括对于灵巧手的情况。我们在网站上发布了代码和额外结果。
英文摘要：
Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to avoid colliding with other objects. In general, we must construct such a 3D interpretation of a complex scene based on limited input, such as a single RGB-D image. We describe SceneComplete, a system for constructing a complete, segmented, 3D model of a scene from a single view. SceneComplete is a novel pipeline for composing general-purpose pretrained perception modules (vision-language, segmentation, image-inpainting, image-to-3D, visual-descriptors and pose-estimation) to obtain highly accurate results. We demonstrate its accuracy and effectiveness with respect to ground-truth models in a large benchmark dataset and show that its accurate whole-object reconstruction enables robust grasp proposal generation, including for a dexterous hand. We release the code and additional results on our website.
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。
