Image segmentation and depth estimation are crucial tasks in computer vision,
especially in autonomous driving scenarios. Although these tasks are typically
addressed separately, we propose an innovative approach to combine them in our
novel deep learning network, Panoptic-DepthLab. By incorporating an additional
depth estimation branch into the segmentation network, it can predict the depth
of each instance segment. Evaluating on Cityscape dataset, we demonstrate the
effectiveness of our method in achieving high-quality segmentation results with
depth and visualize it with a color map. Our proposed method demonstrates a new
possibility of combining different tasks and networks to generate a more
comprehensive image recognition result to facilitate the safety of autonomous
driving vehicles.

图像分割和深度估计是计算机视觉中关键的任务，特别是在自动驾驶场景中。我们基于一种全新的深度学习网络 Panoptic-DepthLab，将这两个任务结合起来，在分割网络中增加了深度估计分支，可以预测每个实例分割的深度。通过在 Cityscape 数据集上的评估，我们证明了我们的方法在实现高质量分割结果且能够通过颜色映射可视化深度方面的有效性。我们的提出的方法展示了将不同任务和网络结合以生成更全面的图像识别结果，以促进自动驾驶车辆的安全的新可能性。