论文标题
MITI：腹腔镜手术的巨大基准测试
MITI: SLAM Benchmark for Laparoscopic Surgery
论文作者
论文摘要
我们提出了一种新的基准测试，用于评估立体视觉惯性计算机视觉算法（SLAM/ SFM/ 3D重建/视觉惯性刻度），用于在腹部微创手术（MIS）干预措施。我们的MITI数据集可通过[https://mediatum.ub.tum.de/1621941]获得，通过完整记录在TUM的研究医院Rechts der Isar，提供了所有必要的数据。它包含来自IMU的多模式传感器信息，立体视频和红外（IR）跟踪作为评估的基础真相。此外，可以使用定向镜，加速度计，磁力计，传感器设置中的刚性变换的校准以及时间休息时间。我们明智地选择了一项合适的干预措施，该干预措施几乎没有切割和组织变形，并用手持式摄像头显示了腹部的完整扫描，因此它是测试SLAM算法的理想选择。为了促进为MIS应用设计的视觉惯性算法的进度，我们希望我们的临床培训数据集有助于并使研究人员能够增强算法。
We propose a new benchmark for evaluating stereoscopic visual-inertial computer vision algorithms (SLAM/ SfM/ 3D Reconstruction/ Visual-Inertial Odometry) for minimally invasive surgical (MIS) interventions in the abdomen. Our MITI Dataset available at [https://mediatum.ub.tum.de/1621941] provides all the necessary data by a complete recording of a handheld surgical intervention at Research Hospital Rechts der Isar of TUM. It contains multimodal sensor information from IMU, stereoscopic video, and infrared (IR) tracking as ground truth for evaluation. Furthermore, calibration for the stereoscope, accelerometer, magnetometer, the rigid transformations in the sensor setup, and time-offsets are available. We wisely chose a suitable intervention that contains very few cutting and tissue deformation and shows a full scan of the abdomen with a handheld camera such that it is ideal for testing SLAM algorithms. Intending to promote the progress of visual-inertial algorithms designed for MIS application, we hope that our clinical training dataset helps and enables researchers to enhance algorithms.
