论文标题
3D成像声纳重建的空间声投影
Spatial Acoustic Projection for 3D Imaging Sonar Reconstruction
论文作者
论文摘要
在这项工作中，我们提出了一种新的方法，用于使用多光束成像声纳重建3D表面。我们从不同的角度从不同的角度来整合了由Sonar测得的强度，以在3D网格中用于固定细胞位置。对于每个单元，我们集成了一个特征向量，该特征向量具有离散视点范围的平均强度。基于充当地面真实信息的特征向量和独立的稀疏范围测量值，我们训练卷积神经网络，使我们能够预测每个单元的签名距离和方向。可以将预测的签名距离投射到沿预测的方向上的截断签名距离字段（TSDF）中。利用行进立方体算法，可以从TSDF呈现多边形网格。我们的方法允许从有限的观点集中进行密集的3D重建，并在三个现实世界数据集上进行了评估。
In this work we present a novel method for reconstructing 3D surfaces using a multi-beam imaging sonar. We integrate the intensities measured by the sonar from different viewpoints for fixed cell positions in a 3D grid. For each cell we integrate a feature vector that holds the mean intensity for a discretized range of viewpoints. Based on the feature vectors and independent sparse range measurements that act as ground truth information, we train convolutional neural networks that allow us to predict the signed distance and direction to the nearest surface for each cell. The predicted signed distances can be projected into a truncated signed distance field (TSDF) along the predicted directions. Utilizing the marching cubes algorithm, a polygon mesh can be rendered from the TSDF. Our method allows a dense 3D reconstruction from a limited set of viewpoints and was evaluated on three real-world datasets.
