Collaborative Deconvolutional Neural Networks for Joint Depth Estimation and Semantic Segmentation.

Area of Science:

• Computer Vision
• Deep Learning
• Scene Understanding

Background:

• Semantic segmentation and single-view depth estimation are crucial computer vision tasks.
• These tasks analyze semantic and geometric image properties, respectively, offering complementary scene understanding insights.
• Existing methods often address these problems independently, limiting potential performance gains.

Purpose of the Study:

• To propose a novel deep learning architecture for the joint modeling of semantic segmentation and single-view depth estimation.
• To leverage the complementary nature of semantic and geometric information for mutual performance enhancement.
• To achieve state-of-the-art results on challenging benchmarks by integrating these two fundamental computer vision tasks.

Main Methods:

• A collaborative deconvolutional neural network (C-DCNN) comprising two DCNNs, one for each task, pretrained with hierarchical supervision.
• Integration of feature maps from both DCNNs using a pointwise bilinear layer to fuse semantic and depth information.
• Simultaneous training for both tasks using sibling classification layers, with depth estimation treated as a classification problem via a soft mapping strategy.

Main Results:

• The proposed C-DCNN effectively fuses semantic and depth features within a unified deep network.
• A soft mapping strategy for depth estimation and a fully connected conditional random field for postprocessing significantly improved performance.
• State-of-the-art results were achieved on the NYU Depth V2 and SUN RGB-D benchmarks for both semantic segmentation and depth estimation.

Conclusions:

• Jointly modeling semantic segmentation and depth estimation in a unified network promotes mutual benefit and improved performance.
• The C-DCNN architecture effectively integrates complementary semantic and geometric scene information.
• The approach demonstrates significant advancements in scene understanding through the synergistic combination of these two fundamental computer vision tasks.