Liu Wenyang, Cheung Yam, Sawant Amit, Ruan Dan
Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095.
Department of Radiation Oncology, University of Texas Southwestern, Dallas, Texas 75390.
Med Phys. 2016 May;43(5):2353. doi: 10.1118/1.4945695.
To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system.
The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method.
On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions.
The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.
开发一种在从三维表面摄影测量系统捕获的点云上进行稳健且实时的表面重建方法。
作者开发了一种在摄影测量系统获取的点云上进行稳健且快速的表面重建方法，无需显式求解典型变分方法所需的偏微分方程。利用所获取点云的超完备性质，他们的方法从点云流形到表面流形求解并传播稀疏线性关系，假设两个流形具有相似的局部几何形状。在相对一致的点云采集情况下，作者提出一种稀疏回归（SR）模型，将目标点云直接近似为训练集的稀疏线性组合，假设由迭代最近点（ICP）建立的点对应关系合理准确且残差误差服从高斯分布。为了适应采集过程中变化的噪声水平和/或不一致遮挡的存在，作者进一步提出一种改进的稀疏回归（MSR）模型，用拉普拉斯先验对由ICP构建的潜在大且稀疏的误差进行建模。作者在一致采集条件下获取的临床点云和存在不一致遮挡的点云上评估了所提出的方法。作者通过将其重建结果与变分方法的结果进行比较，以均方根误差定量评估重建性能。
在临床点云上，SR和MSR模型均实现了亚毫米级的重建精度，并将重建时间减少了两个数量级至亚秒级重建时间。在存在不一致遮挡的点云上，MSR模型展示了其优势，尽管存在引入的遮挡，仍能实现一致且稳健的性能。
作者开发了一种在从三维表面摄影测量系统捕获的点云上进行快速且稳健的表面重建方法，具有亚毫米级的重建精度和亚秒级的重建时间。它适用于放射治疗中的实时运动跟踪，具有清晰的表面结构以便更好地进行量化。
