论文标题
场景完整感知的激光雷达深度完成驾驶场景
Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
论文作者
论文摘要
本文介绍了场景完整的深度完成（SCADC），以完整的LIDAR扫描到具有精细而完整的场景结构的密集深度图中。最近的稀疏度完成的稀疏深度完成仅关注较低的场景，并在上部上产生不规则的估计，因为现有数据集（例如Kitti）不能为上部区域提供地面图。这些区域被认为不太重要，因为它们通常是天空或较少景象的兴趣的树木。但是，我们认为在几种驾驶场景中，例如大型卡车或带负载的汽车，物体可以延伸到场景的上部。因此，具有结构化上场景估计的深度图对于RGBD算法很重要。 SCADC采用立体图像，产生差距更好，但通常比LiDARS精确的差异，以帮助稀疏的LiDAR深度完成。据我们所知，我们是第一个专注于稀疏深度完成的场景完整性的人。我们在深度估计的精度和Kitti的场景完整性上验证了SCADC。此外，我们使用场景完整性的D输入来验证我们的方法，尝试较少探索的室外RGBD语义细分。
This paper introduces Scene Completeness-Aware Depth Completion (SCADC) to complete raw lidar scans into dense depth maps with fine and complete scene structures. Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas. These areas are considered less important since they are usually sky or trees of less scene understanding interest. However, we argue that in several driving scenarios such as large trucks or cars with loads, objects could extend to the upper parts of scenes. Thus depth maps with structured upper scene estimation are important for RGBD algorithms. SCADC adopts stereo images that produce disparities with better scene completeness but are generally less precise than lidars, to help sparse lidar depth completion. To our knowledge, we are the first to focus on scene completeness of sparse depth completion. We validate our SCADC on both depth estimate precision and scene-completeness on KITTI. Moreover, we experiment on less-explored outdoor RGBD semantic segmentation with scene completeness-aware D-input to validate our method.
