Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation

TL;DR

This paper introduces a synthetic data generation method using CycleGAN domain transfer to improve monocular depth estimation, reducing reliance on costly real-world data and enhancing model generalization.

Contribution

It presents a novel approach combining synthetic environments and CycleGAN for domain transfer to generate training data for depth estimation.

Findings

GAN-transformed data performs comparably to real data in depth estimation tasks.

Synthetic data improves model generalization to new environments.

The method reduces the need for extensive real-world data collection.

Abstract

A major obstacle to the development of effective monocular depth estimation algorithms is the difficulty in obtaining high-quality depth data that corresponds to collected RGB images. Collecting this data is time-consuming and costly, and even data collected by modern sensors has limited range or resolution, and is subject to inconsistencies and noise. To combat this, we propose a method of data generation in simulation using 3D synthetic environments and CycleGAN domain transfer. We compare this method of data generation to the popular NYUDepth V2 dataset by training a depth estimation model based on the DenseDepth structure using different training sets of real and simulated data. We evaluate the performance of the models on newly collected images and LiDAR depth data from a Husky robot to verify the generalizability of the approach and show that GAN-transformed data can serve as an effective alternative to real-world data, particularly in depth estimation.