Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation

arXiv:2405.01113v1 Announce Type: new
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 …

abstract algorithms arxiv cs.ai cs.cv data data generation development domain eess.iv images improving major modern quality resolution sensors synthetic synthetic data type