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.

利用定量测试对新鲜离体猪组织进行了全面表征，评估基于激光的飞行时间传感器（lidar）在解剖表面重建方面的质量，表明与基于学习的立体匹配相比，飞行时间传感展示了更高的精度、更低的处理延迟、更高的帧速率和更强的抗传感器距离和光照不好的稳健性。此外，发现近红外光穿透对lidar测量精度的负面影响，肌肉与脂肪和肝脏在深度测量上存在明显差异，研究结果指向结合飞行时间和光谱成像的新方法具有潜力的全程3D感知。

通过激光雷达进行密集三维重建：对离体猪组织的比较研究