Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue
This addresses the challenge of real-time, accurate 3D reconstruction for minimally invasive surgery, though it is incremental as it compares existing sensing methods on new data.
This study evaluated lidar for 3D reconstruction of ex-vivo porcine tissue, finding it outperformed learning-based stereo matching with higher precision, lower delay, higher frame rate, and better robustness to distance and illumination, though noting depth offset issues with muscle tissue.
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.