Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
Jia-Quan Yu Soo-Chang Pei
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.