Panoptic-Depth Color Map for Combination of Depth and Image Segmentation

TL;DR

This paper introduces Panoptic-DepthLab, a deep learning network that combines image segmentation and depth estimation to produce comprehensive scene understanding for autonomous driving, using a novel multi-task approach.

Contribution

It presents a new network architecture that integrates depth estimation into segmentation, enabling simultaneous prediction of instance segments and their depths.

Findings

Achieves high-quality segmentation with depth information on Cityscape dataset.

Demonstrates effective visualization of combined segmentation and depth maps.

Shows potential for improved autonomous vehicle perception systems.

Abstract

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.