Self-supervised depth estimation even in night images using illumination uncertainty with STEPS
Self-supervised depth estimation even in night images using illumination uncertainty with STEPS
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
arXiv paper abstract https://arxiv.org/abs/2302.01334
arXiv PDF paper https://arxiv.org/pdf/2302.01334.pdf
Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D sensing capabilities of self-driving vehicles.
However, it intrinsically relies upon the photometric consistency assumption, which hardly holds during nighttime.
... propose the first method that jointly learns a nighttime image enhancer and a depth estimator, without using ground truth for either task.
... method tightly entangles two self-supervised tasks using a newly proposed uncertain pixel masking strategy.
This strategy originates from the observation that nighttime images not only suffer from underexposed regions but also from overexposed regions.
By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally.
... benchmark the method on two established datasets: nuScenes and RobotCar and demonstrate state-of-the-art performance on both of them ...
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
LinkedIn https://www.linkedin.com/in/morris-lee-47877b7b
#ComputerVision #3D #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning
Comentários