Predicting Depth from Semantic Segmentation using Game Engine Dataset

TL;DR

This paper introduces a novel depth estimation neural network that incorporates semantic labels, demonstrating a 52% reduction in relative error and improved robustness using synthetic game engine datasets.

Contribution

The paper proposes new network structures that integrate semantic labels with images for depth estimation, enhancing accuracy and robustness, especially when trained on synthetic datasets.

Findings

Semantic labels improve depth estimation accuracy.

Synthetic datasets can effectively train depth networks.

Semantic labels increase robustness against domain shift.

Abstract

Depth perception is fundamental for robots to understand the surrounding environment. As the view of cognitive neuroscience, visual depth perception methods are divided into three categories, namely binocular, active, and pictorial. The first two categories have been studied for decades in detail. However, research for the exploration of the third category is still in its infancy and has got momentum by the advent of deep learning methods in recent years. In cognitive neuroscience, it is known that pictorial depth perception mechanisms are dependent on the perception of seen objects. Inspired by this fact, in this thesis, we investigated the relation of perception of objects and depth estimation convolutional neural networks. For this purpose, we developed new network structures based on a simple depth estimation network that only used a single image at its input. Our proposed structures use both an image and a semantic label of the image as their input. We used semantic labels as the output of object perception. The obtained results of performance comparison between the developed network and original network showed that our novel structures can improve the performance of depth estimation by 52\% of relative error of distance in the examined cases. Most of the experimental studies were carried out on synthetic datasets that were generated by game engines to isolate the performance comparison from the effect of inaccurate depth and semantic labels of non-synthetic datasets. It is shown that particular synthetic datasets may be used for training of depth networks in cases that an appropriate dataset is not available. Furthermore, we showed that in these cases, usage of semantic labels improves the robustness of the network against domain shift from synthetic training data to non-synthetic test data.