SCRREAM：SCan、Register、REnder And Map：一个用于标注精确且稠密3D室内场景的框架及基准
SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
AI总结：
本文提出SCRREAM数据集标注框架，通过稠密物体网格标注与真实图像序列相机位姿配准，生成适用于稀疏和稠密3D任务的准确真值，并在11个场景上建立评估基准。
摘要：
传统上，3D室内数据集通常优先考虑规模而非真值准确性，以获得更好的泛化能力。然而，使用这些数据集评估稠密几何任务（如深度渲染）可能存在问题，因为数据集的网格往往不完整，并可能产生错误的真值来评估细节。本文提出SCRREAM，这是一个数据集标注框架，能够标注场景中物体的完全稠密网格，并在真实图像序列上配准相机位姿，从而为稀疏3D和稠密3D任务生成准确真值。我们展示了数据集标注流程的细节，并通过示例场景展示了可从该框架获得的四种可能数据集变体，例如室内重建与SLAM、场景编辑与物体移除、人体重建以及6D位姿估计。近期的室内重建和SLAM流程作为新的基准。与以往室内数据集相比，我们的设计允许在11个示例场景上针对精确渲染的真值深度图评估稠密几何任务。
英文摘要：
Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details. In this paper, we propose SCRREAM, a dataset annotation framework that allows annotation of fully dense meshes of objects in the scene and registers camera poses on the real image sequence, which can produce accurate ground truth for both sparse 3D as well as dense 3D tasks. We show the details of the dataset annotation pipeline and showcase four possible variants of datasets that can be obtained from our framework with example scenes, such as indoor reconstruction and SLAM, scene editing & object removal, human reconstruction and 6d pose estimation. Recent pipelines for indoor reconstruction and SLAM serve as new benchmarks. In contrast to previous indoor dataset, our design allows to evaluate dense geometry tasks on eleven sample scenes against accurately rendered ground truth depth maps.
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。
