通过激光雷达进行密集三维重建：离体猪组织的比较研究
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue
AI总结：
本文通过离体猪组织定量评估激光雷达解剖表面重建，证明其相较学习型立体匹配在精度、延迟、帧率和鲁棒性上的优势，并揭示组织相关近红外穿透深度偏差。
摘要：
新型传感技术和更先进的处理算法正在变革计算机集成手术。尽管研究人员正积极探索用于基于视觉的手术辅助的深度传感和三维重建，但要在微创手术中实现腹腔的实时、准确且鲁棒的三维表示仍然困难。因此，本研究对新鲜离体猪组织进行定量测试，以全面表征基于激光的三维飞行时间传感器（lidar，激光雷达）完成解剖表面重建的质量。研究使用商用激光扫描仪采集真实表面形状，并采用严格的统计工具分析由此产生的带符号误差场。与基于内窥镜图像的现代学习型立体匹配相比，飞行时间传感表现出更高的精度、更低的处理延迟、更高的帧率，以及对传感器距离和不良照明更强的鲁棒性。此外，本文报告了近红外光穿透对不同组织样本中激光雷达测量准确性的潜在负面影响，发现与脂肪和肝脏相比，肌肉存在显著的测量深度偏移。研究结果突显了激光雷达用于术中三维感知的潜力，并指明了结合互补的飞行时间成像与光谱成像的新方法。
英文摘要：
New sensing technologies and more advanced processing algorithms are transforming computer-integrated surgery. While researchers are actively investigating depth sensing and 3D reconstruction for vision-based surgical assistance, it remains difficult to achieve real-time, accurate, and robust 3D representations of the abdominal cavity for minimally invasive surgery. Thus, this work uses quantitative testing on fresh ex-vivo porcine tissue to thoroughly characterize the quality with which a 3D laser-based time-of-flight sensor (lidar) can perform anatomical surface reconstruction. Ground-truth surface shapes are captured with a commercial laser scanner, and the resulting signed error fields are analyzed using rigorous statistical tools. When compared to modern learning-based stereo matching from endoscopic images, time-of-flight sensing demonstrates higher precision, lower processing delay, higher frame rate, and superior robustness against sensor distance and poor illumination. Furthermore, we report on the potential negative effect of near-infrared light penetration on the accuracy of lidar measurements across different tissue samples, identifying a significant measured depth offset for muscle in contrast to fat and liver. Our findings highlight the potential of lidar for intraoperative 3D perception and point toward new methods that combine complementary time-of-flight and spectral imaging.
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。
