Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
This work addresses the need for more comprehensive image recognition to enhance safety in autonomous driving, though it appears incremental as it integrates existing tasks.
The paper tackles the problem of combining image segmentation and depth estimation for autonomous driving by proposing Panoptic-DepthLab, a deep learning network that predicts depth for each instance segment, achieving high-quality results on the Cityscape dataset.
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.