Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
DSER: Spectral Epipolar Representation for Efficient Light Field Depth Estimation
DSER combines spectral epipolar regularization with a hybrid pipeline of gradient initialization, plane-sweeping, multiscale refinement, and occlusion-aware random walk to produce structurally consistent depth maps fr...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.