论文标题
MSDPN：使用多阶段神经网络进行部分激光观察的单眼深度预测
MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks
论文作者
论文摘要
在这项研究中，提出了一个基于深度学习的多阶段网络架构，称为多阶段深度预测网络（MSDPN），以使用2D激光雷达和单眼相机预测密集的深度图。我们提出的网络由多阶段的编码器架构和跨阶段特征聚合（CSFA）组成。提出的多阶段编码器架构体系结构减轻了由2D激光雷达的特征引起的部分观察问题，而CSFA可防止多阶段网络稀释功能，并允许网络学习特征之间的空间间关系。先前的作品使用从地面真实的子采样数据作为输入，而不是实际的2D激光雷达数据。相比之下，我们的方法训练模型并使用物理收集的2D LIDAR数据集进行实验。为此，我们获取了自己的数据集，称为KAIST RGBD-SCAN数据集，并验证了MSDPN在现实条件下的有效性和鲁棒性。经过实验验证，我们的网络可以针对最先进的方法产生有希望的性能。此外，我们分析了不同输入方法的性能，并确认参考深度图在未经训练的情况下是可靠的。
In this study, a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. Our proposed network consists of a multi-stage encoder-decoder architecture and Cross Stage Feature Aggregation (CSFA). The proposed multi-stage encoder-decoder architecture alleviates the partial observation problem caused by the characteristics of a 2D LiDAR, and CSFA prevents the multi-stage network from diluting the features and allows the network to learn the inter-spatial relationship between features better. Previous works use sub-sampled data from the ground truth as an input rather than actual 2D LiDAR data. In contrast, our approach trains the model and conducts experiments with a physically-collected 2D LiDAR dataset. To this end, we acquired our own dataset called KAIST RGBD-scan dataset and validated the effectiveness and the robustness of MSDPN under realistic conditions. As verified experimentally, our network yields promising performance against state-of-the-art methods. Additionally, we analyzed the performance of different input methods and confirmed that the reference depth map is robust in untrained scenarios.
