发表机构
Seoul National University; KAIST（首尔大学; 韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人操作中2D观测与3D世界不匹配的问题，提出校准感知流水线，利用姿态条件和外参可靠性生成度量锚定的可渲染3D高斯数据集，提升新视角保真度并支持操作研究。
AI 中文摘要
机器人操作模型主要从2D观测进行推理，却在3D物理世界中行动。为弥合这一差距，近期工作通过深度、点云和3D轨迹等几何先验增强机器人数据，而可渲染的3D高斯表示则提供了另一种有前景的3D监督形式。然而，3DGS表示主要针对光度保真度设计，可能无法保留真实世界的度量尺度，尤其是在提供的相机外参不可靠时。我们研究了外参可靠性和姿态条件对前馈3DGS的影响，并提出了一种校准感知的流水线，将重建场景锚定到机器人的度量工作空间中。我们的实验表明，姿态条件提高了新视角保真度，而其几何收益取决于注入外参的可靠性。利用该流水线，我们呈现了一个可渲染、度量姿态锚定的数据集，并带有场景级可靠性信息，用于机器人操作研究。我们的数据集可在该https URL获取。
英文摘要
Robot manipulation models primarily reason from 2D observations while acting in the 3D physical world. To bridge this gap, recent work has augmented robot data with geometric priors such as depth, point clouds, and 3D trajectories, while renderable 3D Gaussian representations provide another promising form of 3D supervision. However, 3DGS representation is designed mainly for photometric fidelity and may not preserve real-world metric scale, particularly when the supplied camera extrinsics are unreliable. We study the effect of extrinsic reliability and pose conditioning on feed-forward 3DGS, and propose a calibration-aware pipeline that anchors reconstructed scenes to the robot's metric workspace. Our experiments show that pose conditioning improves novel-view fidelity, while its geometric benefit depends on the reliability of the injected extrinsics. Using this pipeline, we present a renderable, metric-pose-anchored dataset with scene-level reliability information for robot manipulation research. Our dataset is available at https://huggingface.co/datasets/wonguen/3DROID
Comments12 pages, 3 figures
