  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.
