论文标题
Amodal圆锥形分割
Amodal Panoptic Segmentation
论文作者
论文摘要
人类具有显着地感知物体的能力，即使某些部分被遮住了。阿莫达尔感知的这种能力构成了我们对世界的感知和认知理解的基础。为了使机器人能够通过这种能力进行推理，我们制定并提出了一个新的任务，我们将其命名为Amodal Panoptic分割。该任务的目的是同时预测物体类别可见区域的像素语义分割标签，以及事物类别的可见和遮挡区域的实例分割标签。为了促进对这项新任务的研究，我们将两个已建立的基准数据集使用像素级的Amodal Panoptic细分标签，我们可以公开以Kitti-360-APS和BDD100K-APS公开提供。我们提出了几个强大的基线，以及Amodal Panoptic质量（APQ）和Amodal解析覆盖范围（APC）指标，以以可解释的方式量化性能。此外，我们提出了新型的Amodal全景分割网络（APSNET），作为解决此任务的第一步，通过明确建模封闭器和闭塞器之间的复杂关系。广泛的实验评估表明，APSNET在基准上都能达到最先进的性能，更重要的是体现了Amodal识别的实用性。基准可在http://amodal-panoptic.cs.uni-freiburg.de上找到。
Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation. The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes. To facilitate research on this new task, we extend two established benchmark datasets with pixel-level amodal panoptic segmentation labels that we make publicly available as KITTI-360-APS and BDD100K-APS. We present several strong baselines, along with the amodal panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify the performance in an interpretable manner. Furthermore, we propose the novel amodal panoptic segmentation network (APSNet), as a first step towards addressing this task by explicitly modeling the complex relationships between the occluders and occludes. Extensive experimental evaluations demonstrate that APSNet achieves state-of-the-art performance on both benchmarks and more importantly exemplifies the utility of amodal recognition. The benchmarks are available at http://amodal-panoptic.cs.uni-freiburg.de.
